Generating Realistic 3D Hand-Object Interaction Data with HOIDiffusion

Core Concepts

HOIDiffusion enables realistic and diverse 3D hand-object interaction data synthesis through precise structure and appearance control.

Abstract

HOIDiffusion proposes a conditional diffusion model for generating 3D hand-object interaction data. The model disentangles geometry from appearance, offering controllable synthesis based on text and 3D structures. By leveraging a two-stage framework, it first synthesizes the 3D geometric structure of the hand and object, then conditions a diffusion model to generate RGB images. The method outperforms previous approaches in generating physically plausible interactions with flexible control over geometry and appearance. The generated dataset is effective in improving perception systems by training an object pose estimator.

Stats

DexYCB dataset contains almost millions of images but only around 10k videos.
Training process costs approximately 12 hours on eight A100 GPUs.
FID evaluation on 1,000 images shows HOIDiffusion outperforms other models.

Quotes

"A hand is grasping a bowl in [background]."
"A serene beach at sunset."
"A bustling cityscape at night."
"A vibrant desert oasis."

Key Insights Distilled From

HOIDiffusion

by Mengqi Zhang... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.12011.pdf

Deeper Inquiries

How can the divergence in generated images between adjacent frames be mitigated for video generation?

In order to mitigate the divergence in generated images between adjacent frames during video generation, a zero-shot video generation technique can be employed. This technique involves establishing inter-consistency among frames by refactoring the original self-attention layers in the U-Net model to incorporate cross-attention modules between an anchor frame and current frames. By adjusting the model to attend to both the anchor image and themselves, it ensures that each frame maintains awareness of the previous appearance style, thereby promoting consistency throughout the video sequence.

What are the potential applications of HOIDiffusion beyond improving perception systems?

HOIDiffusion has various potential applications beyond improving perception systems. One key application is in data augmentation for downstream tasks such as object 6D pose estimation. By utilizing synthesized images from HOIDiffusion instead of directly rendered object models, it can enhance model performance by providing more realistic and diverse visual features for training. Additionally, HOIDiffusion can be used for generating videos with smooth hand-grasping trajectories through zero-shot video generation techniques, enabling applications in areas requiring dynamic visual content like virtual reality simulations or robotics.

How does HOIDiffusion address the challenge of maintaining diversity in image generation while preventing convergence to fixed styles?

HOIDiffusion addresses the challenge of maintaining diversity in image generation while preventing convergence to fixed styles through appearance regularization techniques. By incorporating background buffers consisting of high-quality scenery images synthesized using pretrained text-to-image diffusion models, along with classifier-free guidance during training, HOIDiffusion ensures that it does not converge quickly to a specific style present in its training dataset. This approach helps maintain flexibility and control over appearance transformations via text prompts without compromising on image diversity or quality.