A lip-sync model with 172 stars: where the paper library's stars came from, and the gap it left

ComplexSync is the WACV 2027 paper code from the AI team at Guangzhou Quwan Technology (the company behind TT Voice and the 'Quwan Qianyin' dubbing platform). It does 'remap the mouth of an existing video to new audio' and focuses on occlusion, side profiles and complex lighting, the kinds of frames where current open-source methods tend to fail. The repo is 43 days old with 172 stars (GitHub, 11-06), which is the scale of an ordinary academic repo. What's worth looking at isn't the star count but how that curve formed, and the few things it doesn't do, which is where others can step in.

A developer waiting on the weights

On Oct 29, 2026, GitHub user hlevring (Henrik Levring) opened the first issue in this repo, titled 'Thanks + questions on release':

The paper is interesting, and congrats on the WACV acceptance. The two-stream training and distillation parts are interesting. Will you release a multi-step model or only the one-step one? I'm looking forward to it. Any rough release date?

At that point the repo had only a README. On Nov 3 the author replied that the inference code and both Stage 1 and Stage 3 weights had been released. The same day hlevring came back saying stage1.pt and stage3.pt weren't on the model page. The author replied:

The weights are still uploading. We had some network trouble on our end, and they should finish within a few days.

Two 5.49 GB files, and they only finished uploading on 11-03 on ModelScope. This issue is nearly the whole story of the repo: people read the paper and waited, and only once the files could actually be downloaded did they start arriving.

What it is

In one line: give it a face video and new audio, and it outputs a video where the mouth matches the new audio and the rest of the face is unchanged. Use cases are digital-human talking heads, short dramas and video translation dubbing. Quwan's own Qianyin product is in that business.

Technically it's a diffusion model distilled to generate in 1 steps. The repo ships two weights: Stage 1 runs 12 steps, and Stage 3 is the 1-step version, additionally fine-tuned on occlusion-repair data. The README says it's an order of magnitude faster than Stage 1. The paper self-reports above 70 FPS, and no third party has reproduced that. It also releases a test set called Complex-Val with 200 'hard' clips.

The README also lists what's not included: training code, data preprocessing, evaluation scripts and Stage 2 are all absent. It only runs on CUDA, and the weights are only on ModelScope, not Hugging Face.

How the stars came

Star counts come from GitHub daily records. Two phases:

Phase one: stars from people waiting for code. The paper went up, got accepted, and the README promised 'coming weeks'. Paper aggregators like alphaXiv and CatalyzeX pick it up automatically, and people who read the paper click star as a placeholder. The +10-31 on 39 came before the code was out. I found no related posts on X, Hacker News, Reddit or Chinese media, and Hugging Face's paper page doesn't list it either. My guess is that some paper aggregator page or a Chinese group was sharing it, but I haven't verified this.

The second stage came once the weights were downloadable. The code 11-01 was already out, but during the three days the weights were still uploading, it only gained two or three stars a day. 11-04 Once the weights could be downloaded, daily gains jumped to 20+ and held for all three days. The people showing up were developers who actually wanted to run it; there are only 4 forks, so almost nobody has modified it yet.

No big account, media outlet or community post pushed it at any point. The company previously put out press releases for Playmate and Playmate2 from the same team, but I found none for this one.

Where the ceiling is

The previous repo from the same author account, Playmate2 (AAAI 2026, multi-person audio-driven animation), is stuck at 300 stars. The top of the open-source lip-sync field looks like this:

MuseTalk and LatentSync are backed by Tencent and ByteDance and came with launch traffic, which is why they have thousands of stars. ComplexSync's technical work may not be worse, but it has only been seen within paper circles. Given the same team's history, it will likely stall at a few hundred stars.

Where it falls short

For someone trying to collect stars, the most useful thing about this repo isn't the model itself but the gaps it leaves:

• The weights aren't on Hugging Face. Overseas developers look for models, like them and run Spaces on HF. ModelScope is unfriendly to them, and hlevring got stuck there for a day. The weights are Apache-2.0 licensed, which allows redistribution.
• No demo you can click. There's no Hugging Face Space and no Colab. The example videos in the README don't come with input files, so you have to bring your own video and audio to run it.
• No ComfyUI node. Most creators building digital humans and short dramas assemble workflows in ComfyUI. The community has already wrapped MuseTalk and LatentSync as nodes (my inference: those node repos also picked up a fair number of stars, though I haven't checked each one).
• No comparisons. The paper says it beats MuseTalk and LatentSync in complex scenes, but nobody has run several models side by side on the same batch of 'hard' videos and published the results.

If it were you

C: you can learn how paper repos gather stars, but following it closely won't win you many stars. The original repo's ceiling is a few hundred stars, and the top spots belong to big-company repos. Wrapping it means you capture only the small overflow from those few hundred stars.

Who it suits: developers already building digital-human, dubbing or AI video workflows who have a GPU and would test lip-sync models anyway. Open-sourcing that testing costs almost nothing. It also suits people building ComfyUI nodes who want to establish a reputation as an AI video node author.

Who it doesn't suit: solo developers who want to train a lip-sync model from scratch to compete with it. You won't have the data, compute or paper to pull stars that way. It also doesn't suit anyone without a CUDA machine who can't run a 5.5 GB set of weights.

How to do it: pick one of three small things and move fast. (1) Move the Stage 3 weights to Hugging Face, add a Gradio Space, cite the arXiv ID in the README so the HF paper page picks it up. (2) Wrap a ComfyUI node and automate the downloads for InsightFace, Whisper and the VAE. (3) The most worthwhile: build a 'complex-scene lip-sync comparison' repo. Use its public Complex-Val set to run ComplexSync, MuseTalk, LatentSync and Wav2Lip on the same batch of occlusion and side-profile videos, then publish side-by-side videos and a speed table. This doesn't depend on any one model, and you can update it every time a new model comes out.

How to get users: the side-by-side comparison videos are themselves shareable content. Post them on X and in r/StableDiffusion and r/comfyui on Reddit. In the original repo's issues, say you've made an HF version or a ComfyUI node. The author will often agree to add it to the README. On the Chinese side, post in groups and WeChat Official Accounts focused on digital humans and short-drama going global.

Before when: within two to three weeks. The weights 11-04 just dropped, and right now is when the first wave of people wants to run it but finds it a hassle. Once the original team adds the HF and demo versions, or a seasoned ComfyUI community member packages it first, the window closes.

What would show this read is wrong: (1) ComplexSync passes 1,000 stars by year-end, or a big account pushes it once. That would mean it becomes the next MuseTalk, and projects that wrap it would do much better than C. (2) Someone ships an HF Space or ComfyUI node first and it gets fewer than 50 stars within a month. That would mean demand here is smaller than it looks. (3) The original team uploads the weights to HF themselves within a week. That kills the first route and leaves only the comparison repo.