Improving Video Generation with Human Feedback

• URL: http://arxiv.org/abs/2501.13918v1
• Date: Thu, 23 Jan 2025 18:55:41 GMT
• Title: Improving Video Generation with Human Feedback
• Authors: Jie Liu, Gongye Liu, Jiajun Liang, Ziyang Yuan, Xiaokun Liu, Mingwu Zheng, Xiele Wu, Qiulin Wang, Wenyu Qin, Menghan Xia, Xintao Wang, Xiaohong Liu, Fei Yang, Pengfei Wan, Di Zhang, Kun Gai, Yujiu Yang, Wanli Ouyang,
• Abstract summary: Video generation has achieved significant advances, but issues like unsmooth motion and misalignment between videos and prompts persist. We develop a systematic pipeline that harnesses human feedback to mitigate these problems and refine the video generation model. We introduce VideoReward, a multi-dimensional video reward model, and examine how annotations and various design choices impact its rewarding efficacy.
• Score: 81.48120703718774
• License:
• Abstract: Video generation has achieved significant advances through rectified flow techniques, but issues like unsmooth motion and misalignment between videos and prompts persist. In this work, we develop a systematic pipeline that harnesses human feedback to mitigate these problems and refine the video generation model. Specifically, we begin by constructing a large-scale human preference dataset focused on modern video generation models, incorporating pairwise annotations across multi-dimensions. We then introduce VideoReward, a multi-dimensional video reward model, and examine how annotations and various design choices impact its rewarding efficacy. From a unified reinforcement learning perspective aimed at maximizing reward with KL regularization, we introduce three alignment algorithms for flow-based models by extending those from diffusion models. These include two training-time strategies: direct preference optimization for flow (Flow-DPO) and reward weighted regression for flow (Flow-RWR), and an inference-time technique, Flow-NRG, which applies reward guidance directly to noisy videos. Experimental results indicate that VideoReward significantly outperforms existing reward models, and Flow-DPO demonstrates superior performance compared to both Flow-RWR and standard supervised fine-tuning methods. Additionally, Flow-NRG lets users assign custom weights to multiple objectives during inference, meeting personalized video quality needs. Project page: https://gongyeliu.github.io/videoalign.

Related papers

• Inference-Time Text-to-Video Alignment with Diffusion Latent Beam Search [23.3627657867351]
  An alignment problem has attracted huge attention, where we steer the output of diffusion models based on some quantity on the goodness of the content. We propose diffusion latent beam search with lookahead estimator, which can select better diffusion latent to maximize a given alignment reward. We demonstrate that our method improves the perceptual quality based on the calibrated reward, without model parameter update.
  arXiv  Detail & Related papers  (2025-01-31T16:09:30Z)
• OnlineVPO: Align Video Diffusion Model with Online Video-Centric Preference Optimization [30.6130504613716]
  We introduce OnlineVPO, a preference learning approach tailored specifically for video diffusion models. By employing the video reward model to offer concise video feedback on the fly, OnlineVPO offers effective and efficient preference guidance.
  arXiv  Detail & Related papers  (2024-12-19T18:34:50Z)
• Prompt-A-Video: Prompt Your Video Diffusion Model via Preference-Aligned LLM [54.2320450886902]
  Text-to-video models have made remarkable advancements through optimization on high-quality text-video pairs. Current automatic methods for refining prompts encounter challenges such as Modality-Inconsistency, Cost-Discrepancy, and Model-Unaware. We introduce Prompt-A-Video, which excels in crafting Video-Centric, Labor-Free and Preference-Aligned prompts tailored to specific video diffusion model.
  arXiv  Detail & Related papers  (2024-12-19T18:32:21Z)
• Autoregressive Video Generation without Vector Quantization [90.87907377618747]
  We reformulate the video generation problem as a non-quantized autoregressive modeling of temporal frame-by-frame prediction. With the proposed approach, we train a novel video autoregressive model without vector quantization, termed NOVA. Our results demonstrate that NOVA surpasses prior autoregressive video models in data efficiency, inference speed, visual fidelity, and video fluency, even with a much smaller model capacity.
  arXiv  Detail & Related papers  (2024-12-18T18:59:53Z)
• TVG: A Training-free Transition Video Generation Method with Diffusion Models [12.037716102326993]
  Transition videos play a crucial role in media production, enhancing the flow and coherence of visual narratives. Recent advances in diffusion model-based video generation offer new possibilities for creating transitions but face challenges such as poor inter-frame relationship modeling and abrupt content changes. We propose a novel training-free Transition Video Generation (TVG) approach using video-level diffusion models that addresses these limitations without additional training.
  arXiv  Detail & Related papers  (2024-08-24T00:33:14Z)
• Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward [118.65089648651308]
  This paper introduces a novel framework that utilizes detailed video captions as a proxy of video content. We show that applying this tailored reward through DPO significantly improves the performance of video LMMs on video Question Answering (QA) tasks.
  arXiv  Detail & Related papers  (2024-04-01T17:28:16Z)
• InstructVideo: Instructing Video Diffusion Models with Human Feedback [65.9590462317474]
  We propose InstructVideo to instruct text-to-video diffusion models with human feedback by reward fine-tuning. InstructVideo has two key ingredients: 1) To ameliorate the cost of reward fine-tuning induced by generating through the full DDIM sampling chain, we recast reward fine-tuning as editing.
  arXiv  Detail & Related papers  (2023-12-19T17:55:16Z)
• Guided Flows for Generative Modeling and Decision Making [55.42634941614435]
  We show that Guided Flows significantly improves the sample quality in conditional image generation and zero-shot text synthesis-to-speech. Notably, we are first to apply flow models for plan generation in the offline reinforcement learning setting ax speedup in compared to diffusion models.
  arXiv  Detail & Related papers  (2023-11-22T15:07:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.