Multi-Condition Latent Diffusion Network for Scene-Aware Neural Human Motion Prediction

TL;DR

This work addresses scene-aware 3D human motion prediction by modeling a probabilistic mapping from past body movements and current 3D scene context to future motion. It introduces Multi-Condition Latent Diffusion (MCLD), which operates in the latent space of a transformer-based VAE and integrates three condition embeddings via a dynamic fusion mechanism within a diffusion framework. Key contributions include a Key Region Proposal to localize interactive scene regions, a Multi-Attention Encoder to capture body dynamics, geometry, and interactions, and a latent diffusion model that jointly reasons over multiple conditions to generate realistic and diverse futures. Experiments on GTA-IM and PROX demonstrate state-of-the-art performance in both accuracy and diversity, highlighting the value of scene context and multi-condition diffusion for practical scene-aware motion prediction.

Abstract

Inferring 3D human motion is fundamental in many applications, including understanding human activity and analyzing one's intention. While many fruitful efforts have been made to human motion prediction, most approaches focus on pose-driven prediction and inferring human motion in isolation from the contextual environment, thus leaving the body location movement in the scene behind. However, real-world human movements are goal-directed and highly influenced by the spatial layout of their surrounding scenes. In this paper, instead of planning future human motion in a 'dark' room, we propose a Multi-Condition Latent Diffusion network (MCLD) that reformulates the human motion prediction task as a multi-condition joint inference problem based on the given historical 3D body motion and the current 3D scene contexts. Specifically, instead of directly modeling joint distribution over the raw motion sequences, MCLD performs a conditional diffusion process within the latent embedding space, characterizing the cross-modal mapping from the past body movement and current scene context condition embeddings to the future human motion embedding. Extensive experiments on large-scale human motion prediction datasets demonstrate that our MCLD achieves significant improvements over the state-of-the-art methods on both realistic and diverse predictions.

Figures (12)

• Figure 1: Three examples of our predicted long-term 3D human motion in the 3D scene. Given the past motion history (blue skeletons), we forecast realistic future pose (red skeletons) with diverse human-scene interactions. The color of the predicted pose is changed over frames.
• Figure 2: Architecture Overview. MCLD consists of a VAE model and a multi-condition latent-based diffusion model. MCLD proposes a two-stage training scheme: first adopting encoding-decoding reconstruction loss to optimize the VAE model and learn effective latent representation $z_{0}$ of future body movements ($i.e.$, $\widehat{B}^{0}, \cdots, \widehat{B}^{\Delta T -1}$); then adopting the noising-denoising strategy to optimize a latent conditional diffusion model and characterize the probabilistic mapping from the joint conditions of past body movements and current scene contexts to future human motion.
• Figure 3: Key Region Proposal Module. Given a 3D scene point cloud ($i.e., S$) and a 3D human motion sequence ($i.e., B^{-}=\{B^{-T}, \dots, B^{-1}\}$) inside it, key region proposal module aims at adaptively demarcating a localized interaction-related 3D region $S^{\prime}$ from $S$ for each $B^{-}$ sample, significantly reducing the redundancy of the initial scene input $S$.
• Figure 4: Multi-Attention Encoder Module. Considering there are multiple dependencies within and between body and scene points, including body motion, scene geometry, and body-scene interaction, we deploy a transformer-based $L_{e}$-layer multi-attention encoder on $S^{\prime}$ and $B^{-}$ to extract these latent embeddings jointly.
• Figure 5: Iterative Denoising Module. Given $\boldsymbol{E}_{B}, \boldsymbol{E}_{I}$ and $\boldsymbol{E}_{S}$ as condition inputs, MCLD learns a conditional denoiser to recursively infer the latent future motion embedding codes $z^{\prime}_{0}$ from a Gaussian noise signal $z_{K}$ with $K$ Markov denoising steps. Then, $\mathcal{D}$ decodes it to 3D motion sequence $\widehat{B}^{+}$.