Real-time video virtual try-on that switches garments during generation, running near 24 fps on a single GPU and claimed 32-180x faster than baselines. Code and model are planned but not yet released.
Research model for real-time video virtual try-on that swaps garments mid-generation on a single GPU.
Updated EU status checked Last shown
FashionChameleon at a glance
- What is FashionChameleon?
- FashionChameleon is a model from Alibaba. Research model for real-time video virtual try-on that swaps garments mid-generation on a single GPU.
- Who makes FashionChameleon?
- FashionChameleon is made by Alibaba.
- Does FashionChameleon work in the EU?
- Unverified. No explicit evidence found; the model is not yet publicly released. Last verified 14 Nov 2026.
- How much does FashionChameleon cost?
- Pricing unknown. No release or pricing; code and model are planned but not yet published.
- Where was FashionChameleon shown?
- In 1 creator video by AI Search. Most recently by AI Search on 24 May 2026, at 45:03 in “AI scientist, DNA editors, AI NPCs, new Qwen, open-source robots, new video editors: AI NEWS”.
Sources: the vendor's own pages (linked above) and creator videos at the linked timestamps. EU status is information, not legal advice; see how we check.
FashionChameleon is a research project from Alibaba-affiliated authors on real-time human-garment customization in video. It performs video virtual try-on in which the garment can be switched while the video is being generated, rather than only before generation starts.
According to a creator summary, it runs at close to 24 fps on a single GPU and is claimed to be 32-180x faster than baseline methods. These figures are unverified here, as the project page text provided gives only a title.
Code and model are reported as planned but not yet released, so it is currently a project page and demo-level research result rather than a usable product or downloadable model. No license, pricing or API has been published.
- Video virtual try-on with garment switching during generation
- Near 24 fps real-time speed on a single GPU (reported)
- Claimed 32-180x faster than baseline methods
- Code and model planned, not yet released
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