SG-I2V: Self-Guided Trajectory Control in Image-to-Video Generation

• URL: http://arxiv.org/abs/2411.04989v1
• Date: Thu, 07 Nov 2024 18:56:11 GMT
• Title: SG-I2V: Self-Guided Trajectory Control in Image-to-Video Generation
• Authors: Koichi Namekata, Sherwin Bahmani, Ziyi Wu, Yash Kant, Igor Gilitschenski, David B. Lindell,
• Abstract summary: Methods for image-to-video generation have achieved impressive, photo-realistic quality. adjusting specific elements in generated videos, such as object motion or camera movement, is often a tedious process of trial and error. We introduce a framework for controllable image-to-video generation that is self-guided.
• Score: 22.693060144042196
• License:
• Abstract: Methods for image-to-video generation have achieved impressive, photo-realistic quality. However, adjusting specific elements in generated videos, such as object motion or camera movement, is often a tedious process of trial and error, e.g., involving re-generating videos with different random seeds. Recent techniques address this issue by fine-tuning a pre-trained model to follow conditioning signals, such as bounding boxes or point trajectories. Yet, this fine-tuning procedure can be computationally expensive, and it requires datasets with annotated object motion, which can be difficult to procure. In this work, we introduce SG-I2V, a framework for controllable image-to-video generation that is self-guided$\unicode{x2013}$offering zero-shot control by relying solely on the knowledge present in a pre-trained image-to-video diffusion model without the need for fine-tuning or external knowledge. Our zero-shot method outperforms unsupervised baselines while being competitive with supervised models in terms of visual quality and motion fidelity.

Related papers

• Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training [51.851390459940646]
  We introduce Latent-Reframe, which enables camera control in a pre-trained video diffusion model without fine-tuning. Latent-Reframe operates during the sampling stage, maintaining efficiency while preserving the original model distribution. Our approach reframes the latent code of video frames to align with the input camera trajectory through time-aware point clouds.
  arXiv  Detail & Related papers  (2024-12-08T18:59:54Z)
• Trajectory Attention for Fine-grained Video Motion Control [20.998809534747767]
  This paper introduces trajectory attention, a novel approach that performs attention along available pixel trajectories for fine-grained camera motion control. We show that our approach can be extended to other video motion control tasks, such as first-frame-guided video editing.
  arXiv  Detail & Related papers  (2024-11-28T18:59:51Z)
• 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)
• WildVidFit: Video Virtual Try-On in the Wild via Image-Based Controlled Diffusion Models [132.77237314239025]
  Video virtual try-on aims to generate realistic sequences that maintain garment identity and adapt to a person's pose and body shape in source videos. Traditional image-based methods, relying on warping and blending, struggle with complex human movements and occlusions. We reconceptualize video try-on as a process of generating videos conditioned on garment descriptions and human motion. Our solution, WildVidFit, employs image-based controlled diffusion models for a streamlined, one-stage approach.
  arXiv  Detail & Related papers  (2024-07-15T11:21:03Z)
• Investigating the Effectiveness of Cross-Attention to Unlock Zero-Shot Editing of Text-to-Video Diffusion Models [52.28245595257831]
  Cross-attention guidance can be a promising approach for editing videos. We show that despite the limitations of current T2V models, cross-attention guidance can be a promising approach for editing videos.
  arXiv  Detail & Related papers  (2024-04-08T13:40:01Z)
• Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions [94.03133100056372]
  Moonshot is a new video generation model that conditions simultaneously on multimodal inputs of image and text. Model can be easily repurposed for a variety of generative applications, such as personalized video generation, image animation and video editing.
  arXiv  Detail & Related papers  (2024-01-03T16:43:47Z)
• DreamVideo: High-Fidelity Image-to-Video Generation with Image Retention and Text Guidance [69.0740091741732]
  We propose a high-fidelity image-to-video generation method by devising a frame retention branch based on a pre-trained video diffusion model, named DreamVideo. Our model has a powerful image retention ability and delivers the best results in UCF101 compared to other image-to-video models to our best knowledge.
  arXiv  Detail & Related papers  (2023-12-05T03:16:31Z)
• ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation [33.37279673304]
  We introduce ConditionVideo, a training-free approach to text-to-video generation based on the provided condition, video, and input text. ConditionVideo generates realistic dynamic videos from random noise or given scene videos. Our method exhibits superior performance in terms of frame consistency, clip score, and conditional accuracy, outperforming other compared methods.
  arXiv  Detail & Related papers  (2023-10-11T17:46:28Z)
• 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)

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.