The Future Is Here: AI Edits Videos, Full‑Body Transfer, Insane 3D Models

Every week the pace of AI advances makes last week look quaint. I’m Alex from AI Search, and in my latest roundup I highlighted a blistering week of releases that matter to Canadian business leaders, creative studios, and technology teams. From Alibaba and Tencent to ByteDance and open‑source research groups, a wave of tools arrived that change how we generate video, 3D content, images and humanlike speech — and many of them are free or open source. If you run a media studio in Toronto, a marketing team in Vancouver, a product shop in Montreal, or a government lab in Ottawa, you should be thinking now about how to test and govern these systems.

Below I unpack the biggest releases, explain real‑world use cases for Canadian companies, flag technical requirements and caveats, and offer practical guidance for early adoption. This is a long read — but it’s the one briefing you’ll want before you brief your board.

What’s in this briefing
Wan Animate: swap characters, transfer full‑body motion — and keep the background
Lucy Edit: semantic, text‑based video editing — free and blazing fast
Hunyuan 3D 3: the new standard for single‑image 3D generation
SRPO and UMO: image models that lift realism and reference transfer
Ling‑Flash 2.0: a fast, efficient MOE for reasoning and code
Tongyi Deep Research: an open, agentic deep‑research model that rivals the big players
Reeve vs Seedream vs NanoBanana: the new battleground in image editing
Audio: Suno v5 (preview), IndexTTS2, VoxCPM and FireRedTTS2 — a new era for voice
Robotics and hardware: Wooji Hand demo
Luma Ray3: HDR, longer thinking — but not yet dominant
Google Chrome’s Gemini integrations: an agentic browser for productivity
Business implications for Canadian organizations — opportunities and red flags
How to pilot these technologies safely in your organization
Ethics, regulation and public trust — what Canadian leaders must consider
Quick reference: technical minimums and where to test today
Practical case studies — how Canadian teams might use these releases
FAQ — Your top questions answered
Final takeaways — what Canadian tech leaders should do today
Closing thought
Further reading and resources

What’s in this briefing

Wan Animate — full‑body motion transfer and character replacement for video
Lucy Edit — fast, text‑based semantic video editing (free + open source)
Hunyuan 3D 3 — the current leading AI 3D model generator
Tencent SRPO and ByteDance UMO — next‑gen image models for realism and reference transfer
Ling Flash 2.0 and Tongyi Deep Research — efficient mixture‑of‑experts models and open research agents
Reeve and Seedream — new image editors competing with NanoBanana
New text‑to‑speech leaders — IndexTTS2, VoxCPM and FireRedTTS2
Suno v5 preview and the future of AI music
Luma Ray3 and Google Chrome’s new Gemini integrations
Robotics demo — the Wooji hand and implications for automation
Business impact, governance and how Canadian organizations should respond
FAQs to help you get started

Wan Animate: swap characters, transfer full‑body motion — and keep the background

Alibaba’s Wan Animate is a watershed moment in video AI. Built on the Wan 2.2 lineage (already one of the best open models for video generation), Wan Animate goes further: it can take an existing reference video and accurately transfer entire body movement — including facial expressions, lip sync and even finger and hand motion — to a different character while preserving the original scene’s background, lighting and ambience.

Why this matters: filmmakers, game cinematics teams, corporate training video producers and advertising houses no longer need to reshoot live action to change talent or characters. You can act out scenes once, then map your performance onto any character. For Canadian creative studios working with limited budgets or tight union schedules, that’s a huge productivity win.

Notable technical details

Open source release: full repo and weights are available; Hugging Face contains model artifacts.
Size: total download around ~72 GB for official assets; running locally currently requires high VRAM (est. ~40 GB), though compressed GGUF builds are already appearing from the community.
Capabilities: full body, hands, face, lip sync; works for humans, creatures, and different animation styles.
Quality: far improved over prior “anyone-animate” tools and even outperforms some paid models in benchmarks; minimal deformation or warping reported in many tests.

Practical considerations for Canadian teams: WAN Animate will be immediately compelling for Toronto-based post houses and Vancouver VFX teams who can allocate compute. But until GGUF and compressed versions are stable and workflows (ComfyUI integrations) are mature, the easiest path is to experiment via cloud or partner with GPU rental providers. Expect a rapid democratization once the community ships lower VRAM builds and turnkey UIs.

Lucy Edit: semantic, text‑based video editing — free and blazing fast

Descartes’ Lucy Edit is a compelling complement to Wan Animate. Think of it as NanoBanana for video: upload a clip, use natural language to change clothing, swap characters, edit hair color — even micro‑edit objects in the frame — and get a new rendered result. The standout is how fast and approachable the playground is.

What Lucy Edit does

Text prompts alter visual appearance and elements in video frames.
Offers both a cloud playground (free credits, pay per generation tiers) and a dev/pro split—dev builds are available for local use.
ComfyUI workflows are available for running Lucy Edit offline; smaller quantized weights (~10 GB) support consumer GPUs with ~12 GB VRAM.

For marketing and creative teams across Canada, Lucy Edit reduces friction for last‑mile edits and iterative A/B testing. Imagine a small digital agency in Calgary using Lucy Edit to produce multiple variations of a hero ad with differing wardrobe colours or props to test audience response — in hours instead of days.

Hunyuan 3D 3: the new standard for single‑image 3D generation

Tencent’s HunYen (branded as Hunyuan 3D 3.0) is a step change in single‑image 3D generation. Upload one image — whether a 2D drawing, a real photograph or a concept piece — and Hunyuan 3D 3 predicts missing geometry and texture to output an “ultra‑HD” 3D model with realistic faces, body contours and pose fidelity.

Why Hunyuan 3D matters

For studios and indie developers: dramatically reduces the time to build quality 3D assets from concept art.
For product teams: prototype physical products or character assets for AR/VR experiences with minimal modeling effort.
For education: simplifies curriculum for 3D fundamentals and rapid design iteration.

Hunyuan 3D’s tooling is free to try after signing up; new users receive free credits to experiment. The UI allows you to upload multiple reference angles for improved accuracy and to select face count (polygon density) depending on fidelity vs. size tradeoffs.

SRPO and UMO: image models that lift realism and reference transfer

This week also saw two image model plays that are worth Canadian teams’ attention: Tencent’s SRPO and ByteDance’s UMO.

SRPO (Tencent)

SRPO is a fine‑tuning of the Flux model, trained to improve aesthetic realism and reduce the synthetic “plastic” look common to earlier generative outputs. In tests it’s stronger on amateur photo realism, lighting fidelity and architectural detail. While the full SRPO model is large (~50 GB), community GGUFs are appearing that reduce VRAM needs to consumer levels (e.g., Q2 builds ~4 GB).

UMO (ByteDance)

UMO specializes in style and reference transfer. Upload character references and UMO can convincingly place those characters into new photographic scenes, change clothing, and compositely rearrange multiple characters in a single shot. Crucially, ByteDance provides Hugging Face spaces for quick experimentation and ComfyUI workflows for local runs. Base models are modest in size (<2 GB in Uno versions), making UMO very accessible.

These two models emphasize a clear trend: image generators are moving beyond stereotyped “AI” faces toward grounded, diverse, and contextually accurate renderings. Canadian marketers and broadcasters can use this to produce culturally relevant material that represents Canada’s diversity more authentically, provided governance safeguards are in place.

Ling‑Flash 2.0: a fast, efficient MOE for reasoning and code

Inclusion AI’s Ling‑Flash 2.0 is a mixture‑of‑experts (MoE) model that punches far above its weight. With a total parameter count of 100 billion but only ~6.1 billion active parameters at runtime, Ling‑Flash delivers state‑of‑the‑art performance comparable to much larger models on reasoning, coding and other benchmarks — while remaining extremely efficient.

Why MoE and Ling‑Flash matter to enterprises

Lower inference cost for equivalent performance — great for production deployment in customer service, developer tools and analytics.
Faster token throughput: Ling‑Flash claims 200+ tokens/sec, which is attractive for latency‑sensitive applications.
Open source availability: teams can test performance and fine‑tune locally before committing to vendor contracts.

For Canadian software companies and fintechs, Ling‑Flash 2.0 presents a path to deploy powerful reasoning and code generation capabilities in‑house, improving cost predictability and data governance.

Tongyi Deep Research: an open, agentic deep‑research model that rivals the big players

Alibaba’s Tongyi Deep Research is the most striking release this week. It’s an agentic, deep‑research system designed to autonomously perform complex, multi‑step research: web crawling, code execution, evidence synthesis and long‑running thought processes. Benchmarks show it outperforms several large proprietary “deep research” systems, despite being far smaller in active size.

Technical highlights

Architecture: mixture‑of‑experts with ~30B total params and only ~3B active params during inference.
Deep research heavy mode: spawns multiple agents researching different facets of a problem, then consolidates findings.
Performance: in benchmarked tasks (complex filtering queries, long math proofs, multi‑step web research) Tongyi matched or exceeded closed models like OpenAI Deep Research and Gemini Deep Research.

Real‑world implications: think about corporate due diligence, regulatory research, forensic investigations, or long‑form market analysis. Tongyi can autonomously run dozens of searches, synthesize cross‑source evidence, and return a structured report — potentially replacing large parts of a human research team’s initial legwork. For Canadian consultancies and think tanks, this is both an efficiency lever and a disruption.

But there are legal considerations: web scraping policies, licensing of sources, and handling proprietary paywalled content all require legal review. Canadian organizations should ensure compliance with terms of service and consider IP protections before delegating research.

Reeve vs Seedream vs NanoBanana: the new battleground in image editing

Two weeks after Google’s NanoBanana and ByteDance’s Seedream 4.0, Reeve emerged as another contender in the micro‑edit image editor race. Reeve brings a powerful positional editing approach: it automatically detects objects in an image and allows you to drag, resize, and semantically edit individual elements without touching the rest of the scene.

Where Reeve shines

Micro edits: change cup colours, swap a dog species, resize objects, and alter composition without global disruption.
Object‑level control: automatic object detection and boundaries let you manipulate subjects precisely.
Free tier: sign up and get free credits for quick testing.

Limitations: Reeve currently lags Seedream and NanoBanana on tight character consistency tasks, model sheet generation and some complex spatial prompts (like satellite‑to‑street‑front transformations). But its object positional control is a differentiator that creative agencies and product teams will appreciate.

Audio: Suno v5 (preview), IndexTTS2, VoxCPM and FireRedTTS2 — a new era for voice

Audio generation continues to accelerate. Suno teased V5 with a short demo that hints at higher quality, particularly in vocals. More immediately practical are three open releases that matter for Canadian enterprises: IndexTTS2 (an expressive, emotion‑controlled TTS), VoxCPM (a powerful voice cloning and multi‑emotion system), and FireRedTTS2 (multilingual, multi‑speaker support with longer outputs).

VoxCPM: voice cloning, emotion, accent transfer

VoxCPM can clone voices with only a few seconds of reference audio and adapt emotion, accent and language. Notable capabilities demonstrated include:

Cloning a four‑second clip to generate natural speech in different languages.
Detecting transcript emotion and producing matching intonation (angry, surprised, etc.).
Transferring accents and even background noise characteristics.

VoxCPM also supports phoneme hints to fix hard pronunciations, making it useful for technical narration and multilingual localization.

FireRedTTS2: multi‑speaker and long output

FireRed supports up to four speakers, three‑minute generations and multiple languages. The tool accepts reference audio and transcript pairs and can produce multi‑voice dialogues. For contact centers, eLearning, and multimedia localisation in Canada’s bilingual market, FireRed is particularly compelling.

Business use cases and governance

Marketing personalization: dynamic voice messaging tailored to customer segments.
Accessibility: generating high‑quality narration for content accessibility in both official languages.
Localization: clone local accents for better cultural relevance (with strong consent and IP controls).

But a blunt caution: the same tools that enable efficiency also facilitate voice spoofing and misinformation. Canadian enterprises should adopt consent policies, voice consent capture, watermarking and legal agreements before cloning audio from employees or public figures.

Robotics and hardware: Wooji Hand demo

On the hardware front, the Wooji Hand demo showcases a life‑scale robotic hand with 20 active degrees of freedom, high‑resolution tactile sensors and impressive dexterity — spinning pens, handling chopsticks, lifting heavy loads and manipulating fragile items. While the demo is teleoperated, the tactile integration and strength suggest near‑term applications in manufacturing, healthcare and logistics.

For Canadian advanced manufacturing clusters — notably in Ontario and Quebec — a dexterous, robust robotic hand could automate complex assembly tasks that previously required human finesse. That could reshape labour models and supply chains, requiring policy responses around reskilling.

Luma Ray3: HDR, longer thinking — but not yet dominant

Luma’s Ray3 is their latest video model, promising 16‑bit HDR outputs and improved physics reasoning. The platform exposes a “thinking” log while it plans keyframes (a transparent step‑by‑step generation approach) and offers a free tier to try lower‑quality previews.

Testing shows Ray3 handles mid‑shot, static or simple movements well (portraits, seated people, simple eating scenarios). But it struggles with complex physics, fast acrobatics, juggling and nuanced limb movements — areas where models like Hilo v2, V03 and Kling 2.1 currently perform better.

Bottom line: Ray3 is a strong entrant and will be valuable for business uses that need HDR aesthetic and fast drafts, but it is not yet the top pick for high‑fidelity action or strict anatomical consistency.

Google Chrome’s Gemini integrations: an agentic browser for productivity

Google integrated Gemini directly into Chrome, bringing a conversational agent into the right‑top of the browser and exposing an “AI mode” in the address bar. On first look, it’s a productivity multiplier:

Context awareness: Gemini can reference the current tab and answer questions about page content.
Agentic workflows (coming soon): demos show the agent can shop for items in an email list and navigate e‑commerce sites — with user takeover options.
History inspection: ask Gemini to retrieve pages you saw last week or show items you researched for team planning.

For Canadian enterprises, integrated agents in the browser accelerate research, procurement, and summarization. But the features’ availability is US‑first, with rollouts elsewhere to follow. IT teams should track controls, data residency and admin options before broad deployment.

Business implications for Canadian organizations — opportunities and red flags

These releases collectively form an inflection point. They are not incremental improvements; they change workflows across creative, marketing, research and operations. Below I distill the most actionable implications for Canadian technologists and business leaders.

1. Creative and media production — radical cost and time savings

Full‑body motion transfer (Wan Animate) and semantic video editing (Lucy Edit) compress pre‑production and reshoot cycles. Canadian broadcasters, indie film studios and ad agencies can iterate on casting, wardrobe and blocking without expensive reshoots. Hunyuan 3D reduces 3D asset overhead for AR/VR and game studios, accelerating prototype throughput.

2. Localization and accessibility — scale at low marginal cost

VoxCPM and FireRed let companies produce voiceovers in multiple languages, with accent and tone. Governments and healthcare organizations in Canada can scale bilingual content channels more affordably, improving inclusivity — but only if consent and accuracy controls are enforced.

3. Research and knowledge work — faster, but verify

Tongyi Deep Research and Ling‑Flash 2.0 show open models can deliver deep, multi‑step reasoning. Consultancies, legal teams and financial analysts can use these agents for triage and initial research. But outputs must be verified: agents access and synthesize many sources and can propagate errors without human oversight.

4. Operational automation and customer service

Efficient MoE models (Ling‑Flash) and high‑quality TTS enable chatbots and voice bots with better reasoning and more humanlike responses. Canadian contact centres can reduce average handling times and improve customer satisfaction while controlling onshore data storage.

5. Legal, ethical and reputational risks

Every powerful generative tool raises deep issues: deepfakes, voice cloning without consent, IP ownership of generated assets, web scraping legality for research agents, and biased or inaccurate outputs affecting marginalized communities. The Canadian legal framework (PIPEDA, provincial privacy laws) plus emerging federal consultation on AI governance mean companies should deploy a principled approach:

Document training and inference data lineage.
Obtain explicit consent for any employee voice cloning or likeness usage.
Use watermarks or verifiable provenance for generated media used publicly.
Validate critical outputs via human review and third‑party audits.

How to pilot these technologies safely in your organization

Here’s a practical step‑by‑step for CIOs, CTOs and creative leads to move from curiosity to controlled pilots.

1. Identify high‑value, low‑risk pilots

Choose non‑public, internal projects (training videos, internal marketing) where mistakes won’t be public.
Pick use cases with measurable ROI: faster iteration on campaign hero shots, reduced localization time per asset, or research time cut by X%.

2. Establish a governance playbook

Consent: written permission for voice/likeness cloning.
Review: a human in the loop for every productionized output.
Logging: retain input, prompt, and model metadata to enable audits.

3. Start with managed access and cloud execution

Many of these models are heavy to run locally today. Use cloud compute for initial experiments, then move to on‑premise or hybrid setups when performance and cost metrics justify the shift.

4. Train the team and involve legal early

Give creatives and product managers a half‑day lab to experiment, and involve legal/compliance to set acceptable use cases and red lines before external publication.

5. Measure and iterate

Track speedups, cost per asset, error rates and downstream QA time. Use those KPIs to decide when to scale a pilot into production.

Ethics, regulation and public trust — what Canadian leaders must consider

The Canadian context is unique: bilingual obligations, privacy laws, and public sector procurement standards mean organizations cannot treat generative AI as a simple vendor swap. Expect scrutiny from stakeholders.

Privacy and consent: voice and likeness cloning must be opt‑in with clear revocation mechanisms.
Data residency: models that require uploading to foreign cloud providers may need special approvals for certain regulated industries.
Copyright and IP: auto‑generated content may contain elements learned from copyrighted sources; have clear IP assignment clauses and review for reuse risks.
Transparency: label synthetic media when used in public facing contexts; this preserves trust and reduces regulatory risk.

Quick reference: technical minimums and where to test today

Here’s a pragmatic cheat sheet of where to experiment depending on your hardware.

Wan Animate: Official weights large (~72 GB); expect high VRAM requirements (40+ GB) unless using community GGUFs. Best tried on cloud GPUs for now.
Lucy Edit: Dev weights compressed (~10 GB), runnable on consumer GPUs (12 GB). ComfyUI workflows available.
Hunyuan 3D 3: Web UI available with free credits; best for rapid prototyping without heavy local compute.
SRPO: Official model ~50 GB; community compressed GGUFs bring it down to ~4 GB in some builds.
UMO: Lightweight base models (<2 GB); Hugging Face spaces for immediate testing.
Ling‑Flash 2.0: Hugging Face release; optimized for speed and lower latency (6B active).
Tongyi Deep Research: Large (~60 GB) but available for download with instructions; expect multi‑GPU setups for local runs.
VoxCPM / FireRedTTS2 / IndexTTS2: Hugging Face spaces available for immediate testing; VoxCPM notably tiny in some configurations (~0.5B params) and low latency on a 4090.
Ray3 (Luma): Sign up for cloud access; free tier supports low‑res, short durations.

Practical case studies — how Canadian teams might use these releases

Below are three short scenarios that illustrate immediate, actionable pilots for Canadian organizations.

Case study 1: Toronto ad agency — rapid hero variations

A mid‑sized agency in Toronto runs a week‑long pilot using Lucy Edit and Reeve to produce 30 hero ad variations for A/B testing. They record a single actor on a neutral set and use Wan Animate to map the performance to three brand mascots. Lucy Edit handles wardrobe changes and Reeve fine‑tunes cup colours and background props without reshoots.

Outcome: The agency reduces shoot time by 60%, lowers production costs by 40% and completes multivariate testing in a single sprint.

Case study 2: Vancouver game studio — prototype 3D assets

A VR game studio uses Hunyuan 3D 3.0 to convert 2D character concepts into high‑fidelity 3D models. Designers iterate through textures and back views without waiting for modeling sprints. The studio mixes the outputs with manual retopology to ensure performance budgets are met.

Outcome: Prototype iteration time drops from two weeks to two days, accelerating early playtests and investor demos.

Case study 3: Public health communications — bilingual, accessible messaging

A provincial public health office pilots FireRedTTS2 to produce accessible audio translations of high importance public guidance in English and French, while logging consent and metadata. Voice cloning is limited to a pool of trained professional narrators who have signed waivers.

Outcome: Faster deployment of audio advisories, improved accessibility and measurable engagement improvements among visually impaired and francophone populations.

FAQ — Your top questions answered

Q: Are these models safe to use in production right away?

A: They are powerful and useful for many production workflows, but “safe” depends on governance. Start with internal, non‑public pilots, set consent and attribution policies, and keep a human‑in‑the‑loop for any content that will be published or used for decision‑making.

Q: How do I run Wan Animate or Tongyi locally?

A: Both projects have GitHub repos and Hugging Face releases. Expect large downloads (tens of GB). For Wan Animate, you’ll likely need high VRAM unless community GGUF compressions are mature. Tongyi Deep Research similarly requires multiple consumer GPUs for local inference. Many teams will prefer cloud GPU rentals for initial experiments.

Q: What about legal risks for voice cloning and image generation?

A: Use explicit, recorded consent for any voice or likeness cloning. For public figures, adhere to local publicity laws and reputation risk policies. Avoid deploying synthesized media that could mislead or impersonate without clear labeling. Consult legal counsel on IP and source data usage.

Q: Can these models run on commodity hardware in small agencies?

A: Yes — many models (UMO, Lucy Edit dev weights, compressed SRPO, VoxCPM small builds) are engineered for consumer GPUs. The community is rapidly shipping GGUF compressed versions that reduce VRAM needs to 6–12 GB. Where models remain large, cloud testing is a viable entry point.

Q: How should a Canadian CIO evaluate vendor risk?

A: Evaluate model provenance (open vs closed), data residency, access control, vulnerability to prompt injection, and how the vendor manages model updates. Prefer vendors that offer transparency, audit logs and fine‑grained governance features.

Q: What are recommended first pilots for a midsize business?

A: Start with:

Internal training videos using Lucy Edit and Wan Animate (consented actors).
Localization of evergreen marketing content using FireRedTTS2 or VoxCPM (with professional voice consent).
Prototype market research using Tongyi Deep Research for hypothesis generation, but always have analysts verify outputs.

Final takeaways — what Canadian tech leaders should do today

We’re in the middle of a generational shift: open models and rapid community tooling mean the best capabilities are no longer locked behind massive vendor barriers. That’s a huge win for Canadian innovation — but it also raises governance obligations.

Action checklist for leaders:

Set up a cross‑functional AI pilot team (IT, legal, security, creative) and allocate a small budget for cloud GPU experiments.
Run two proof‑of‑value projects in the next quarter: one in creative/media (Lucy Edit, Hunyuan 3D) and one in knowledge work (Ling‑Flash or Tongyi for internal research triage).
Create or update AI acceptable‑use policies focusing on voice, likeness and IP.
Invest in staff training and upskilling in prompt engineering, reviewing model outputs and checking provenance.
Engage with local peers — incubators, universities and industry associations — to co‑develop best practices for public sector and regulated industries.

These steps balance speed and safety while letting your organization capture early advantage.

Closing thought

“AI never sleeps.” That line isn’t just a quip — it’s a reality. The rhythm of innovation demands that Canadian businesses move from passive observers to informed experimenters. The tools released this week give us the means to cut costs, create richer experiences, and analyze knowledge faster than ever. Do it responsibly, test quickly, and build governance into every rollout.

Which of these tools are you most excited to test in your organization? Are you looking at creative pilots, audio localization, or autonomous research agents? Share your plans with colleagues, and if you’re in Canada and want to collaborate on pilots or governance frameworks, drop a comment or reach out through your industry network — the work is too important to do alone.