UniVid: The Open-Source Unified Video Model
Unified video modeling that combines generation and understanding
capabilities is increasingly important but faces two key challenges:
maintaining semantic faithfulness during flow-based generation due to
text-visual token imbalance and the limitations of uniform cross-modal
attention across the flow trajectory, and efficiently extending image-centric
MLLMs to video without costly retraining. We present UniVid, a unified
architecture that couples an MLLM with a diffusion decoder through a
lightweight adapter, enabling both video understanding and generation. We
introduce Temperature Modality Alignment to improve prompt adherence and
Pyramid Reflection for efficient temporal reasoning via dynamic keyframe
selection. Extensive experiments on standard benchmarks demonstrate
state-of-the-art performance, achieving a 2.2% improvement on VBench-Long total
score compared to EasyAnimateV5.1, and 1.0% and 3.3% accuracy gains on MSVD-QA
and ActivityNet-QA, respectively, compared with the best prior 7B baselines.
68dba731d2bf1f4b15ec7947
Jiabin Luo
68dba731d2bf1f4b15ec7948
Junhui Lin
claimed_verified
2025-10-02T13:56:32.587Z
6618fac36b786b742e0b19af
/avatars/5642b6cf1539b3b140d28cd16c02b456.svg
Junhui Lin
user
ljhlrn
68dba731d2bf1f4b15ec7949
Zeyu Zhang
claimed_verified
2025-09-30T10:17:30.244Z
64ec877bb93654d4ca5c92e9
https://cdn-avatars.huggingface.co/v1/production/uploads/64ec877bb93654d4ca5c92e9/GvHk_KSdE9Rhnk_o-NaZX.jpeg
Zeyu Zhang
user
SteveZeyuZhang
68dba731d2bf1f4b15ec794a
Biao Wu
68dba731d2bf1f4b15ec794b
Meng Fang
68dba731d2bf1f4b15ec794c
Ling Chen
68dba731d2bf1f4b15ec794d
Hao Tang
UniVid: The Open-Source Unified Video Model
Unified video modeling that combines generation and understanding
capabilities is increasingly important but faces two key challenges:
maintaining semantic faithfulness during flow-based generation due to
text-visual token imbalance and the limitations of uniform cross-modal
attention across the flow trajectory, and efficiently extending image-centric
MLLMs to video without costly retraining. We present UniVid, a unified
architecture that couples an MLLM with a diffusion decoder through a
lightweight adapter, enabling both video understanding and generation. We
introduce Temperature Modality Alignment to improve prompt adherence and
Pyramid Reflection for efficient temporal reasoning via dynamic keyframe
selection. Extensive experiments on standard benchmarks demonstrate
state-of-the-art performance, achieving a 2.2% improvement on VBench-Long total
score compared to EasyAnimateV5.1, and 1.0% and 3.3% accuracy gains on MSVD-QA
and ActivityNet-QA, respectively, compared with the best prior 7B baselines.
UniVid combines video understanding and generation using an MLLM with a diffusion decoder, achieving state-of-the-art performance through Temperature Modality Alignment and Pyramid Reflection.
flow-based generation text-visual token imbalance uniform cross-modal attention diffusion decoder Temperature Modality Alignment Pyramid Reflection VBench-Long MSVD-QA ActivityNet-QA