ByTheWay: Boost Your Text-to-Video Generation Model to Higher Quality in a Training-free Way
Jiazi Bu, Pengyang Ling, Pan Zhang, Tong Wu, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Dahua Lin, Jiaqi Wang
摘要
CPII under InnoHK, A dog, playing on the grass, soft lightening, high quality, ... A panda plays in the snow covered forest, 4k resolution, film grain, ... Vanilla ByTheWay Vanilla ByTheWay Enhancing structural plausibility and temporal consistency Enriching motion patterns and amplifying the motion magnitude Figure 1. Unlock the potential of pretrained text-to-video (T2V) generation models in a training-free approach. (1) ByTheWay helps to enhance structural plausibility and temporal consistency in generated videos, significantly reducing artifacts and flickering. (2) ByTheWay contributes to enriching motion patterns and amplifying the motion magnitude in generated videos. Further, ByTheWay can be seamlessly integrated into various powerful T2V backbones (e.g., AnimateDiff[12] and VideoCrafter2[6]) in a plug-and-play manner, serving as a highly extensible module without introducing additional parameters or sampling cost.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper3
- CapRL: Stimulating Dense Image Caption Capabilities via Reinforcement LearningLong Xing, Xiaoyi Dong, Yuhang Zang, Yuhang Cao 等ICLR 2026 · 被引用 37 次
- HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned GuidanceJiazi Bu, Pengyang Ling, Yujie Zhou, Pan Zhang 等NeurIPS 2025 · 被引用 21 次
- Fine-Grained GRPO for Precise Preference Alignment in Flow ModelsYujie Zhou, Pengyang Ling, Jiazi Bu, Yibin Wang 等CVPR 2026 · 被引用 19 次
它引用的顶会 Paper24
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 被引用 35,902 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- Denoising Diffusion Implicit ModelsJiaming Song, Chenlin Meng, Stefano ErmonICLR 2021 · 被引用 11,743 次
- Photorealistic Text-to-Image Diffusion Models with Deep Language UnderstandingChitwan Saharia, William Chan, Saurabh Saxena, Lala Li 等NeurIPS 2022 · 被引用 8,965 次
- Adding Conditional Control to Text-to-Image Diffusion ModelsLvmin Zhang, Anyi Rao, Maneesh AgrawalaICCV 2023 · 被引用 6,759 次
相关 Paper
- Encapsulated Composition of Text-to-Image and Text-to-Video Models for High-Quality Video SynthesisTongtong Su, Chengyu Wang, Bingyan Liu, Jun Huang 等CVPR 2025
- DynamicsBoost: Dynamic Plausible Video Generation via Annotation-Free Continuation Preference OptimizationJiaxing Li, Jiepeng Wang, Junyao Gao, Yang Liu 等CVPR 2026 · 被引用 2 次
- VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion ModelsYabo Zhang, Yuxiang Wei, Xianhui Lin, Zheng Hui 等AAAI 2025 · 被引用 3 次
- Improving Motion in Image-to-Video Models via Adaptive Low-Pass GuidanceJune Suk Choi, Kyungmin Lee, Sihyun Yu, Yisol Choi 等CVPR 2026 · 被引用 4 次
- FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video GenerationAriel Shaulov, Itay Hazan, Lior Wolf, Hila CheferNeurIPS 2025 · 被引用 22 次
