Versatile Transition Generation with Image-to-Video Diffusion

• URL: http://arxiv.org/abs/2508.01698v1
• Date: Sun, 03 Aug 2025 10:03:56 GMT
• Title: Versatile Transition Generation with Image-to-Video Diffusion
• Authors: Zuhao Yang, Jiahui Zhang, Yingchen Yu, Shijian Lu, Song Bai,
• Abstract summary: We present a Versatile Transition video Generation framework that can generate smooth, high-fidelity, and semantically coherent video transitions.<n>We show that VTG achieves superior transition performance consistently across all four tasks.
• Score: 89.67070538399457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Leveraging text, images, structure maps, or motion trajectories as conditional guidance, diffusion models have achieved great success in automated and high-quality video generation. However, generating smooth and rational transition videos given the first and last video frames as well as descriptive text prompts is far underexplored. We present VTG, a Versatile Transition video Generation framework that can generate smooth, high-fidelity, and semantically coherent video transitions. VTG introduces interpolation-based initialization that helps preserve object identity and handle abrupt content changes effectively. In addition, it incorporates dual-directional motion fine-tuning and representation alignment regularization to mitigate the limitations of pre-trained image-to-video diffusion models in motion smoothness and generation fidelity, respectively. To evaluate VTG and facilitate future studies on unified transition generation, we collected TransitBench, a comprehensive benchmark for transition generation covering two representative transition tasks: concept blending and scene transition. Extensive experiments show that VTG achieves superior transition performance consistently across all four tasks.

Related papers

• EfficientMT: Efficient Temporal Adaptation for Motion Transfer in Text-to-Video Diffusion Models [73.96414072072048]
  Existing motion transfer methods explored the motion representations of reference videos to guide generation.<n>We propose EfficientMT, a novel and efficient end-to-end framework for video motion transfer.<n>Our experiments demonstrate that our EfficientMT outperforms existing methods in efficiency while maintaining flexible motion controllability.
  arXiv  Detail & Related papers  (2025-03-25T05:51:14Z)
• 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)
• Enhancing Video-Language Representations with Structural Spatio-Temporal Alignment [130.15775113897553]
  Finsta is a fine-grained structural-temporal alignment learning method. It consistently improves the existing 13 strong-tuning video-language models.
  arXiv  Detail & Related papers  (2024-06-27T15:23:36Z)
• TC-Bench: Benchmarking Temporal Compositionality in Text-to-Video and Image-to-Video Generation [97.96178992465511]
  We argue that generated videos should incorporate the emergence of new concepts and their relation transitions like in real-world videos as time progresses. To assess the Temporal Compositionality of video generation models, we propose TC-Bench, a benchmark of meticulously crafted text prompts, corresponding ground truth videos, and robust evaluation metrics.
  arXiv  Detail & Related papers  (2024-06-12T21:41:32Z)
• MAVIN: Multi-Action Video Generation with Diffusion Models via Transition Video Infilling [19.004339956475498]
  MAVIN is designed to generate transition videos that seamlessly connect two given videos, forming a cohesive integrated sequence. We introduce a new metric, CLIP-RS (CLIP Relative Smoothness), to evaluate temporal coherence and smoothness, complementing traditional quality-based metrics. Experimental results on horse and tiger scenarios demonstrate MAVIN's superior performance in generating smooth and coherent video transitions.
  arXiv  Detail & Related papers  (2024-05-28T09:46:09Z)
• 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)
• Dual-MTGAN: Stochastic and Deterministic Motion Transfer for Image-to-Video Synthesis [38.41763708731513]
  We propose Dual Motion Transfer GAN (Dual-MTGAN), which takes image and video data as inputs while learning disentangled content and motion representations. Our Dual-MTGAN is able to perform deterministic motion transfer and motion generation. The proposed model is trained in an end-to-end manner, without the need to utilize pre-defined motion features like pose or facial landmarks.
  arXiv  Detail & Related papers  (2021-02-26T06:54:48Z)
• Motion-Attentive Transition for Zero-Shot Video Object Segmentation [99.44383412488703]
  We present a Motion-Attentive Transition Network (MATNet) for zero-shot object segmentation. An asymmetric attention block, called Motion-Attentive Transition (MAT), is designed within a two-stream encoder. In this way, the encoder becomes deeply internative, allowing for closely hierarchical interactions between object motion and appearance.
  arXiv  Detail & Related papers  (2020-03-09T16:58:42Z)

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.