InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion (2308.16905v1)

Abstract: This paper addresses a novel task of anticipating 3D human-object interactions (HOIs). Most existing research on HOI synthesis lacks comprehensive whole-body interactions with dynamic objects, e.g., often limited to manipulating small or static objects. Our task is significantly more challenging, as it requires modeling dynamic objects with various shapes, capturing whole-body motion, and ensuring physically valid interactions. To this end, we propose InterDiff, a framework comprising two key steps: (i) interaction diffusion, where we leverage a diffusion model to encode the distribution of future human-object interactions; (ii) interaction correction, where we introduce a physics-informed predictor to correct denoised HOIs in a diffusion step. Our key insight is to inject prior knowledge that the interactions under reference with respect to contact points follow a simple pattern and are easily predictable. Experiments on multiple human-object interaction datasets demonstrate the effectiveness of our method for this task, capable of producing realistic, vivid, and remarkably long-term 3D HOI predictions.

InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion

Recent advancements in the field of 3D human-object interactions (HOIs) have demonstrated significant potential for applications in robotics, animation, and computer vision. Despite these advancements, current research often falls short in addressing the complexity of dynamic HOIs and the whole-body interactions with objects. The paper "InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion" proposes a novel task focused on predicting 3D HOIs in a more dynamic and realistic manner, addressing these gaps through a framework named InterDiff.

Summary

The paper introduces InterDiff, a framework designed to anticipate 3D HOIs that involve diverse objects and require modeling full-body motion as well as ensuring physical validity of interactions. This framework is particularly innovative due to its two-step approach: interaction diffusion and interaction correction. Interaction diffusion utilizes a diffusion model to forecast the distribution of potential future interactions based on a sequence of historical frames. Interaction correction is implemented through a physics-informed predictor that adjusts the denoised predictions to ensure plausible interactions by leveraging physical laws.

The diffusion model uses a Denoising Diffusion Probabilistic Model (DDPM) to encode future interactions probabilistically. In contrast, the correction phase employs a physics-informed strategy to prevent issues such as penetration and floating, often seen in naive predictions. This phase relies on the insight that short-term object motion, when referenced correctly to contact points, can simplify interaction prediction.

Key Insights and Methodology

InterDiff is characterized by the following noteworthy insights and methodologies:

1. Interaction Diffusion: It encodes interaction patterns using a DDPM conditioned on past HOIs and involves both the human and object states. The authors effectively employ transformer architectures conditioned by object shape features extracted using PointNet.
2. Interaction Correction: This involves a novel physics-informed mechanism that corrects the predicted sequences. The core principle is transforming object motion into a coordinate system aligned with the contact points identifies predictable motion patterns, which simplifies the prediction task. This is achieved using a spatial-temporal graph neural network (STGNN) to predict relative motion.
3. Training and Evaluation: The framework was evaluated using multiple human-object datasets, such as BEHAVE and GRAB, demonstrating its capability to handle generalization across different objects and interaction scenarios. The model was shown to outperform RNN and VAE-based baselines significantly in metrics like MPJPE for humans and rotation/translation errors for objects.
4. Long-term and Diverse Predictions: The framework can make autoregressive predictions that not only span longer durations but also exhibit a range of plausible future scenarios, enhancing its applicability in dynamic environments.

Implications and Future Directions

The InterDiff framework's non-reliance on post-hoc optimization techniques or extensive physical simulators signifies a pivotal shift towards more efficient and scalable prediction models in complex environments. The work underscores the importance of integrating physical validity within generative models, paving the way for more advanced simulation capabilities in robotics and virtual reality applications.

From a theoretical standpoint, InterDiff contributes to a deeper understanding of incorporating physical laws into diffusion dynamics, which can be further explored and refined. Practically, the adaptation of such models to broader interaction contexts, including multi-agent systems and deformable objects, remains an enticing avenue for future research. The resolution of interactions involving multiple dynamic entities or more complex object constructs could further extend the utility of models like InterDiff.

Thus, InterDiff stands as a significant contribution to the domain of human-object interaction prediction, offering insight and capability to develop systems capable of predicting and understanding complex, dynamic interactions.