Previously, creating “digital human” videos like those from HeyGen required purchasing a membership, and each video produced was charged individually. This situation may now change. Meituan’s technical team has officially open-sourced a commercial-grade digital human video generation model called “LongCat-Video-Avatar 1.5.” Compared to previous closed-source solutions like HeyGen and Kling Avatar, LongCat-Video-Avatar 1.5 shows significant improvements in lip sync accuracy, physical plausibility, long-video stability, multi-person interaction, and inference efficiency. It is freely available under MIT license, allowing developers to deploy directly in local environments and perform batch generation.
Three Core Upgrades: From Research to Commercialization
LongCat-Video-Avatar 1.5 addresses the three major pain points of traditional digital human technology—jittering, distortion, and high latency—through systematic improvements.
pic.twitter.com/sO3u04Mt2q
我操！美团直接把商业闭源Avatar干翻了，
开源免费版LongCat-Video-Avatar-1.5来了！塞一张照片 + 一段语音（中文英语日语随便来），
直接出唇同步爆炸、自然眨眼摇头、手势乱飞的说话视频。长视频脸不崩、多人对话各管各的、
唱歌跳舞都行，动漫动物真人全吃得下！…— HankAI (@hank_aibtc) May 25, 2026
Audio Encoder Upgrade: Whisper-large Replaces Wav2Vec2
The previous version used Wav2Vec2 as the audio encoder, while version 1.5 was upgraded to OpenAI’s Whisper-large. The larger parameter count and richer multilingual prior knowledge enable the model to capture phonetic variations and pronunciation rhythm more precisely. This change directly delivers three benefits:
- More accurate lip movements for complex audio such as long sentences, fast speech, and singing
- More natural coordination of facial, head, and body movements
- Frame skipping rate and identity drift are significantly reduced in long videos
Multi-Stage Data Augmentation System: Enhancing Open-Domain Generalization
The team established a new data processing pipeline, combining offline annotation with online validation mechanisms and incorporating three specific data types to enable stable model performance across diverse scenarios including real people, virtual idols, anime, and animals:
- Multi-person conversation dataAccurately distinguish speakers from listeners through speaker detection that eliminates audio-visual ambiguity
- Silent dataVideos without dialogue help the model learn natural micro-expressions during silence, preventing random mouth movements when the mouth should stay still.
- Emotional dataFrame-level emotion recognition filters and injects emotion changes, enabling true synchronization between voice and facial expressions.
Hand and Consistency-Specific Alignment: Introducing GRPO
For scenarios with frequent hand movements such as e-commerce livestreaming and product demonstrations, the model introduces GRPO (Group Relative Policy Optimization) human preference alignment mechanism:
- Add first frame hand detection mechanism
- Optimizing Hand Generation Using Frame-Level Reward Signals
- Effectively improve hand distortion, localized structural collapse, and inconsistent movement
15x faster inference: 10-second video in just 1 minute
To reduce commercial computing costs, LongCat-Video-Avatar 1.5 has undergone significant architectural optimizations:
- DMD TechnologyAdopting Distributed Matching Distillation to compress the generation process from 50 steps to 8 steps
- Architecture refactoringReplace the traditional three-model parallel approach with a single shared base model plus multiple LoRA adapters to free up GPU memory.
- actual performanceInference efficiency improved by approximately 15x, with a 10-second video taking only about 1 minute to generate.
This means it can run on a single NVIDIA H200 (80GB VRAM), significantly lowering the hardware barrier for digital human content creation.
Benchmarks comprehensively outperform closed-source solutions
In the EvalTalker benchmark, 770 evaluators and 10 domain experts rated various scenarios including news, education, and entertainment, and LongCat-Video-Avatar 1.5 comprehensively outperformed major closed-source solutions such as Kling Avatar 2.0, OmniHuman-1.5, and HeyGen:
- User Preference Win Rate: 65.9% vs Kling Avatar 2.0, 61.1% vs OmniHuman-1.5, 54.3% vs HeyGen
- Single-person rating of 3.336, significantly higher than HeyGen
- With a multi-person rating of 2.730, it greatly surpasses InfiniteTalk’s 2.339
- The main subject deformation rate is only 23.1%, the background deformation rate is 9.4%, and the frame skipping rate is only 0.8%, making it the best among all compared models.
Versatile Applications: From E-commerce Live Streaming to Anime Generation
LongCat-Video-Avatar 1.5’s applications go well beyond typical digital human tools. According to official demonstrations, the model supports the following scenarios:
- E-commerce marketingInput reference photos and audio to generate lip-synced, identity-consistent product promotion videos
- Long Video GenerationNatively supports minute-level video with no color drift or quality loss
- Group chatMultiple characters each stay composed, naturally taking turns speaking
- singing performanceCombines dynamic action with musical expressiveness
- anime charactersSupports stable rendering of both stylized characters and 2D digital humans
- animal characterseven driving the lip movements and expressions of non-human characters
Open Source Resources and Community Response
The model is now available on multiple platforms:
- GitHub：meituan-longcat/LongCat-Video
- Hugging Face (with trial):meituan-longcat/LongCat-Video-Avatar-1.5
The community response was enthusiastic, with tech media and the developer community generally considering this one of the most important releases in the open-source video generation space in 2026. YouTube creators noted that LongCat-Video is currently the best open-source competitor to OpenAI Sora 2, with quality already on par with Sora 2. Reddit’s r/LocalLLaMA community also quickly started…Discussionrecognize its breakthrough in the digital human field
Outlook
The open-sourcing of LongCat-Video-Avatar 1.5 not only bridges the largest gap between open-source and commercial solutions in the digital human field, but also provides content creators, e-commerce platforms, and educational institutions with a low-cost, high-efficiency video generation solution. With the openness of the MIT license, more developers are expected to engage in secondary development and optimization based on this model, further promoting the popularization of digital human technology. For teams that have long relied on closed-source services like HeyGen, this may be an opportunity to transition to open-source solutions.
Source: KOCPC Chinese