arXiv AI
Oct 9

Conditional Residual Prediction: Improving Autoregressive Video Diffusion without a Bidirectional Teacher

The paper introduces Conditional Residual Prediction (CRP), a technique that reduces a causal video diffusion model’s dependence on past frames by having the model first predict the current frame without conditioning on history and then adding a residual based on the past. This approach allows training a causal model from an image‑model initialization without requiring a bidirectional teacher or complex distillation, and it nearly closes a 6.14‑point quality gap to a bidirectional model. Using CRP, the authors train Optica, a 2‑billion‑parameter causal video model that generates 5‑second 480p videos and achieves an 82.78 score on VBench with only about 15 million training videos.

By Bowen Zheng, Zhiguang Liu, Jiarong Ou, Rui Chen, Tianyang Hu
arXiv Computer Vision
Sep 25

ViRDM: Taming Representation Distribution Matching for Few-Step Causal Video Generation

ViRDM is a new post‑training method for few‑step causal video generation that eliminates the need for a large teacher model and an online critic. By applying representation distribution matching (RDM) with a precomputed target distribution, a lightweight VAE decoder, and staged vector–Jacobian products, ViRDM overcomes memory, optimization, and temporal dynamics challenges. The approach reduces GPU memory usage and training time, achieving state‑of‑the‑art VBench performance with only 20 generator updates and 16 A100 GPU‑hours.

By Zichong Meng, Chongjian Ge, Chun-Hao P. Huang, Yang Zhou, Huaizu Jiang