Bringing physics-based controller policies to virtual reality
Nowadays, it is easy to set up any element of a VR experience so users can interact with it physically, except for interactive VR characters. Can we extend this to animated VR characters? In this work, we studied whether physics-based character animation (PBCA) controllers can produce high-quality movement in VR-compatible environments (Unity3D, WebXR). We evaluated the quality of the results, as well as the losses at each stage of the processing pipeline: data ingest, training and inference. For data ingest, we computed accuracy metrics related to the retargeting method used. For training, we computed accuracy metrics related to different training policies and different training environments. We developed new JAX implementations of state-of-the-art PBCA controllers (DeepMimic, AMP and ADD) which can be trained in MuJoCo-MJX in minutes, 50 times faster than in a traditional CPU-based training environment. The benefit of these particular implementations is that the resulting control policies can be readily transferred to Unity3D and support cross-platform compilation, including Android, something critical for use in VR production. For inference, we tested these controller policies in Unity3D and WebXR. Our results suggest that DeepMimic and ADD trained in MuJoCo-MJX generate controller policies with better animation fidelity than other methods. To simplify the comparison between controller policies, and different reference animations, we implemented an animation fidelity index (afi). Its results are analogous to other more common metrics, but afi is more intuitive to interpret and thus simplifies the comparison of training methods across the successive stages of the processing pipeline.