Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance (2403.14781v2)

Abstract: In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion guidance in curernt human generative techniques. The methodology utilizes the SMPL(Skinned Multi-Person Linear) model as the 3D human parametric model to establish a unified representation of body shape and pose. This facilitates the accurate capture of intricate human geometry and motion characteristics from source videos. Specifically, we incorporate rendered depth images, normal maps, and semantic maps obtained from SMPL sequences, alongside skeleton-based motion guidance, to enrich the conditions to the latent diffusion model with comprehensive 3D shape and detailed pose attributes. A multi-layer motion fusion module, integrating self-attention mechanisms, is employed to fuse the shape and motion latent representations in the spatial domain. By representing the 3D human parametric model as the motion guidance, we can perform parametric shape alignment of the human body between the reference image and the source video motion. Experimental evaluations conducted on benchmark datasets demonstrate the methodology's superior ability to generate high-quality human animations that accurately capture both pose and shape variations. Furthermore, our approach also exhibits superior generalization capabilities on the proposed in-the-wild dataset. Project page: https://fudan-generative-vision.github.io/champ.

• The paper integrates a 3D SMPL model with latent diffusion to achieve controllable and precise human image animation.
• It introduces a multi-layer motion fusion module with self-attention to blend shape and motion cues for cross-identity animation.
• Extensive experiments on benchmark datasets demonstrate superior image quality and temporal consistency over state-of-the-art methods.

An Academic Evaluation of "Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance"

The paper "Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance" presents a noteworthy advancement in the field of human image animation by incorporating a 3D parametric model within the latent diffusion framework. This consists primarily of the Skinned Multi-Person Linear (SMPL) model, which serves as a comprehensive representation of both body shape and pose, facilitating the generation of more precise human animations from static images.

Core Contributions

This work introduces a novel methodology that harnesses the SMPL model to mitigate common challenges faced by prior techniques, such as inadequate shape alignment and inconsistent motion guidance. The authors effectively utilize SMPL-rendered depth images, normal maps, and semantic maps along with skeleton-based motion guidance to condition latent diffusion models. These models are enhanced by integrating a multi-layer motion fusion module equipped with self-attention mechanisms, which supports spatial fusion of shape and motion latent representations. This results in the ability to generate temporally consistent and accurate human animations.

Methodological Insights

The framework leverages latent diffusion models, a technique increasingly popular due to its computational efficiency and capacity to yield high-quality results. By concentrating the diffusion and denoising processes within a latent space, this approach integrates parametric human modeling with generative models efficiently. The incorporation of SMPL not only provides a unified representation of human body features but also enhances cross-identity animation, which is a significant advantage over previous skeleton-only based models. Further, the use of the SMPL model facilitates parametric shape alignment, enabling more consistent translation of reference images to animated motions derived from various sources.

Experimental Evaluation

The authors have conducted extensive experiments on multiple benchmark datasets, including TikTok and an internally proposed diverse dataset. The results highlight the methodology's superiority over current state-of-the-art approaches, including prominent GAN-based and diffusion-based methods like MRAA, DisCo, and MagicAnimate. This new approach exhibited noticeable improvements in quantitative metrics such as PSNR, SSIM, and LPIPS, as well as better FID-VID and FVD scores, indicating enhanced image quality and video consistency.

Implications and Future Directions

This research introduces a mechanism that could greatly impact areas requiring realistic human animation, such as virtual reality, interactive storytelling, and digital content creation. The combination of SMPL model and latent diffusion models represents a significant step forward in moving beyond the constraints of traditional methods, especially in capturing nuanced pose and shape dynamics. Future research might explore further optimization of the SMPL model integration, particularly in enhancing facial and hand animations where current models face limitations. Advanced models or hybrid approaches that combine SMPL's robustness in shape representation with detailed feature-based methods could potentially resolve these challenges.

In conclusion, the paper makes a substantial contribution to the field of human image animation by presenting a methodology that marries the detail-oriented capabilities of 3D parametric modeling with the generative prowess of latent diffusion models. While certain limitations remain, the paper lays a robust groundwork for future exploration and development in creating sophisticated and realistic animated human figures.