SG-I2V: Self-Guided Trajectory Control in Image-to-Video Generation (2411.04989v3)

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 significantly narrowing down the performance gap with supervised models in terms of visual quality and motion fidelity. Additional details and video results are available on our project page: https://kmcode1.github.io/Projects/SG-I2V

• The paper introduces a zero-shot trajectory control framework that leverages semantic feature alignment in pre-trained video diffusion models to generate high-quality videos from static images.
• The method circumvents the need for extensive fine-tuning or annotated datasets by manipulating latent semantic features early in the video synthesis process.
• Experimental results demonstrate superior FID, FVD, and Object Motion Control metrics compared to existing unsupervised and supervised approaches.

Self-Guided Trajectory Control in Image-to-Video Generation: A Technical Overview of SG-I2V

The paper "SG-I2V: Self-Guided Trajectory Control in Image-to-Video Generation" introduces a novel approach for achieving controllable video generation from static images. This approach leverages the inherent capabilities of a pre-trained image-to-video diffusion model to achieve precise control over object trajectories without the computational overhead typically associated with model fine-tuning or reliance on extensive, annotated datasets.

Overview of Methods and Approach

The proposed method, SG-I2V, advances the field of image-to-video generation by offering zero-shot trajectory control. This is achieved without degrading visual quality, a common challenge faced by unsupervised methods. SG-I2V exploits the semantic knowledge embedded in video diffusion models, allowing for adjustments in object motion and camera dynamics directly from input images.

The underlying process involves manipulating the semantic features extracted during the early stages of video synthesis through a diffusion model, specifically identifying and altering key self-attention layer outputs. Unlike existing tuning-free methods dependent on text prompts, SG-I2V operates in an image-only setting. By performing semantic feature alignment across video frames, it leverages the inherent structure of diffusion models to control scene elements along specified trajectories. This method circumvents the traditionally arduous process of fine-tuning on large datasets and instead optimizes the generation process through effective latent space manipulation and a unique frequency-based post-processing step to maintain output quality.

Key Contributions

The paper’s primary contributions include:

1. Analysis of Semantic Feature Alignment: A detailed exploration of semantic feature alignments within video diffusion models, highlighting key differences from image diffusion models. The analysis identifies the challenges of weak cross-frame feature alignment, which are addressed to enable effective trajectory control.
2. SG-I2V Framework: A novel zero-shot strategy for controlling image-to-video generation is introduced, utilizing the pre-existing capabilities of video diffusion models without additional external guidance or data refinement. The method integrates trajectory control seamlessly into the video generation task, a capability not conventionally present in text-based image-to-video methods.
3. Superior Performance Metrics: Experimentation confirms that SG-I2V outperforms unsupervised baselines and remains competitive with supervised counterparts in terms of visual fidelity. This achievement is underlined by strong FID and FVD scores as well as impressive Object Motion Control (ObjMC) results, attesting to its precise motion fidelity.

Implications and Future Directions

The introduction of zero-shot trajectory control presents several practical and theoretical implications. Practically, SG-I2V reduces computational resources and laborious labeling efforts typical of video generation tasks, making it highly attractive for applications requiring quick adaptability to new image inputs without retraining. Theoretically, the findings around semantic alignment in feature maps suggest the potential for further breakthroughs in understanding the underpinnings of diffusion-based video generation models, which could lead to enhanced architectures and methodologies.

Future work as hinted by the authors could pivot toward addressing limitations such as handling large object motions and reducing potential artifacts resulting from out-of-distribution latents. Furthermore, extending this framework to newer video generation models could leverage evolving model capabilities, potentially increasing the scope and quality of generated content.

In summary, SG-I2V contributes a significant advancement in the domain of video synthesis by establishing a robust methodology rooted in existing model capabilities, which balances efficiency, rendering quality, and user-directed control in video generation tasks.