Skip-and-Play: Depth-Driven Pose-Preserved Image Generation for Any Objects

TL;DR

This paper introduces Skip-and-Play (SnP), a novel depth-driven pose control method for image generation that improves controllability across diverse objects and poses by mitigating shape dependency issues.

Contribution

The paper proposes a new depth-based pose control technique with SnP, enabling pose-preserved image generation for various objects and poses, overcoming limitations of existing methods.

Findings

SnP outperforms baseline models in pose accuracy and diversity.

SnP can generate images with different objects and prompts, demonstrating robustness.

Depth-based control with SnP enhances controllability in diffusion models.

Abstract

The emergence of diffusion models has enabled the generation of diverse high-quality images solely from text, prompting subsequent efforts to enhance the controllability of these models. Despite the improvement in controllability, pose control remains limited to specific objects (e.g., humans) or poses (e.g., frontal view) due to the fact that pose is generally controlled via camera parameters (e.g., rotation angle) or keypoints (e.g., eyes, nose). Specifically, camera parameters-conditional pose control models generate unrealistic images depending on the object, owing to the small size of 3D datasets for training. Also, keypoint-based approaches encounter challenges in acquiring reliable keypoints for various objects (e.g., church) or poses (e.g., back view). To address these limitations, we propose depth-based pose control, as depth maps are easily obtainable from a single depth estimation model regardless of objects and poses, unlike camera parameters and keypoints. However, depth-based pose control confronts issues of shape dependency, as depth maps influence not only the pose but also the shape of the generated images. To tackle this issue, we propose Skip-and-Play (SnP), designed via analysis of the impact of three components of depth-conditional ControlNet on the pose and the shape of the generated images. To be specific, based on the analysis, we selectively skip parts of the components to mitigate shape dependency on the depth map while preserving the pose. Through various experiments, we demonstrate the superiority of SnP over baselines and showcase the ability of SnP to generate images of diverse objects and poses. Remarkably, SnP exhibits the ability to generate images even when the objects in the condition (e.g., a horse) and the prompt (e.g., a hedgehog) differ from each other.