LeviTor: 3D Trajectory Oriented Image-to-Video Synthesis

Abstract: The intuitive nature of drag-based interaction has led to its growing adoption for controlling object trajectories in image-to-video synthesis. Still, existing methods that perform dragging in the 2D space usually face ambiguity when handling out-of-plane movements. In this work, we augment the interaction with a new dimension, i.e., the depth dimension, such that users are allowed to assign a relative depth for each point on the trajectory. That way, our new interaction paradigm not only inherits the convenience from 2D dragging, but facilitates trajectory control in the 3D space, broadening the scope of creativity. We propose a pioneering method for 3D trajectory control in image-to-video synthesis by abstracting object masks into a few cluster points. These points, accompanied by the depth information and the instance information, are finally fed into a video diffusion model as the control signal. Extensive experiments validate the effectiveness of our approach, dubbed LeviTor, in precisely manipulating the object movements when producing photo-realistic videos from static images. Our code is available at: https://github.com/ant-research/LeviTor.

• The paper presents a novel method integrating 3D trajectory control into image-to-video synthesis, overcoming traditional 2D limitations.
• It leverages K-means clustering and depth information to produce intuitive control signals for diffusion-based video generation.
• Experimental results demonstrate significant improvements in video quality metrics, enabling realistic and controllable object movements.

Overview of the LeviTor Paper: 3D Trajectory Oriented Image-to-Video Synthesis

The paper "LeviTor: 3D Trajectory Oriented Image-to-Video Synthesis" presents a novel approach to image-to-video synthesis by focusing on incorporating 3D trajectory control into the video generation process. This work addresses the limitations of traditional 2D dragging methods which struggle to accurately interpret out-of-plane movements in video synthesis. The proposed method enables users to draw trajectories with an added depth dimension, thus facilitating more realistic and controlled object movements in generated videos.

Methodology

LeviTor introduces a method to control 3D object trajectories by leveraging depth information combined with strategically clustered points from object masks, providing an intuitive user interface to input 3D trajectory data. Key elements of LeviTor’s methodology include:

1. Trajectory Representation: The authors utilize K-means clustering to abstract object masks into a set of centroid points. These points, coupled with depth information, form the core control signal fed into a video diffusion model for video synthesis.
2. Integration with Diffusion Models: The control signal derived from the clustered points and depth data is integrated into diffusion-based models to drive the generation of video sequences that adhere to the specified 3D trajectory.
3. User Interaction: The paper presents an inference pipeline that allows users to input 3D trajectories on 2D images by adjusting point depths interactively, thereby simplifying the process and making it accessible to users without requiring extensive technical expertise.

Experimental Results

The paper details extensive experimental validation of the proposed approach. LeviTor is shown to generate more realistic and consistent object movements in videos compared to existing methodologies, such as DragNUWA and DragAnything, which rely solely on 2D inputs. The authors report significant improvements in both quantitative and qualitative assessments, measuring metrics such as Frechet Video Distance (FVD) and Frechet Inception Distance (FID), to confirm the efficacy of their model in producing high-quality videos with accurate and controllable motion.

Implications and Future Work

LeviTor's advancement in integrating 3D trajectory control into image-to-video synthesis presents substantial implications for areas in computer graphics, virtual reality, and interactive media, where precision in rendering realistic object movements within dynamic scenes is critical. The paper speculates that future work may explore enhancing the system's ability to handle non-rigid object transformations and more complex motion dynamics by integrating more sophisticated video base models.

The research bridges a gap in trajectory-based video generation by providing a mechanism that blends ease of use with the complexity required to synthesize videos that respect both user intentions and realistic physical interactions. The methodology's potential to democratize video synthesis by lowering the barrier for creating high-fidelity animated content is a significant advancement for content creators.

In conclusion, LeviTor represents a promising step towards more refined control mechanisms in the field of video generation, offering insights and tools that can be further refined and expanded through future research and technological advancements.