HuViDPO:Enhancing Video Generation through Direct Preference Optimization for Human-Centric Alignment

• URL: http://arxiv.org/abs/2502.01690v1
• Date: Sun, 02 Feb 2025 16:55:42 GMT
• Title: HuViDPO:Enhancing Video Generation through Direct Preference Optimization for Human-Centric Alignment
• Authors: Lifan Jiang, Boxi Wu, Jiahui Zhang, Xiaotong Guan, Shuang Chen,
• Abstract summary: We introduce the strategy of Direct Preference Optimization (DPO) into text-to-video (T2V) tasks. Existing T2V generation methods lack a well-formed pipeline with exact loss function to guide the alignment of generated videos with human preferences.
• Score: 13.320911720001277
• License:
• Abstract: With the rapid development of AIGC technology, significant progress has been made in diffusion model-based technologies for text-to-image (T2I) and text-to-video (T2V). In recent years, a few studies have introduced the strategy of Direct Preference Optimization (DPO) into T2I tasks, significantly enhancing human preferences in generated images. However, existing T2V generation methods lack a well-formed pipeline with exact loss function to guide the alignment of generated videos with human preferences using DPO strategies. Additionally, challenges such as the scarcity of paired video preference data hinder effective model training. At the same time, the lack of training datasets poses a risk of insufficient flexibility and poor video generation quality in the generated videos. Based on those problems, our work proposes three targeted solutions in sequence. 1) Our work is the first to introduce the DPO strategy into the T2V tasks. By deriving a carefully structured loss function, we utilize human feedback to align video generation with human preferences. We refer to this new method as HuViDPO. 2) Our work constructs small-scale human preference datasets for each action category and fine-tune this model, improving the aesthetic quality of the generated videos while reducing training costs. 3) We adopt a First-Frame-Conditioned strategy, leveraging the rich in formation from the first frame to guide the generation of subsequent frames, enhancing flexibility in video generation. At the same time, we employ a SparseCausal Attention mechanism to enhance the quality of the generated videos.More details and examples can be accessed on our website: https://tankowa.github.io/HuViDPO. github.io/.

Related papers

• Improving Video Generation with Human Feedback [81.48120703718774]
  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.
  arXiv  Detail & Related papers  (2025-01-23T18:55:41Z)
• 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)
• OpenHumanVid: A Large-Scale High-Quality Dataset for Enhancing Human-Centric Video Generation [27.516068877910254]
  We introduce OpenHumanVid, a large-scale and high-quality human-centric video dataset. Our findings yield two critical insights: First, the incorporation of a large-scale, high-quality dataset substantially enhances evaluation metrics for generated human videos. Second, the effective alignment of text with human appearance, human motion, and facial motion is essential for producing high-quality video outputs.
  arXiv  Detail & Related papers  (2024-11-28T07:01:06Z)
• T2V-Turbo-v2: Enhancing Video Generation Model Post-Training through Data, Reward, and Conditional Guidance Design [79.7289790249621]
  Our proposed method, T2V-Turbo-v2, introduces a significant advancement by integrating various supervision signals. We highlight the crucial importance of tailoring datasets to specific learning objectives. We demonstrate the potential of this approach by extracting motion guidance from the training datasets and incorporating it into the ODE solver.
  arXiv  Detail & Related papers  (2024-10-08T04:30:06Z)
• VideoGuide: Improving Video Diffusion Models without Training Through a Teacher's Guide [48.22321420680046]
  VideoGuide is a novel framework that enhances the temporal consistency of pretrained text-to-video (T2V) models. It improves temporal quality by interpolating the guiding model's denoised samples into the sampling model's denoising process. The proposed method brings about significant improvement in temporal consistency and image fidelity.
  arXiv  Detail & Related papers  (2024-10-06T05:46:17Z)
• ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation [37.05422543076405]
  Image-to-video (I2V) generation aims to use the initial frame (alongside a text prompt) to create a video sequence. Existing methods often struggle to preserve the integrity of the subject, background, and style from the first frame. We propose ConsistI2V, a diffusion-based method to enhance visual consistency for I2V generation.
  arXiv  Detail & Related papers  (2024-02-06T19:08:18Z)
• E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning [53.63364311738552]
  Bio-inspired event cameras or dynamic vision sensors are capable of capturing per-pixel brightness changes (called event-streams) in high temporal resolution and high dynamic range. It calls for events-to-video (E2V) solutions which take event-streams as input and generate high quality video frames for intuitive visualization. We propose textbfE2HQV, a novel E2V paradigm designed to produce high-quality video frames from events.
  arXiv  Detail & Related papers  (2024-01-16T05:10:50Z)
• Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning [50.60891619269651]
  Control-A-Video is a controllable T2V diffusion model that can generate videos conditioned on text prompts and reference control maps like edge and depth maps. We propose novel strategies to incorporate content prior and motion prior into the diffusion-based generation process. Our framework generates higher-quality, more consistent videos compared to existing state-of-the-art methods in controllable text-to-video generation.
  arXiv  Detail & Related papers  (2023-05-23T09:03:19Z)
• Make-A-Video: Text-to-Video Generation without Text-Video Data [69.20996352229422]
  Make-A-Video is an approach for translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V) We design a simple yet effective way to build on T2I models with novel and effective spatial-temporal modules. In all aspects, spatial and temporal resolution, faithfulness to text, and quality, Make-A-Video sets the new state-of-the-art in text-to-video generation.
  arXiv  Detail & Related papers  (2022-09-29T13:59:46Z)

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.