Synthesizing Physical Character-Scene Interactions (2302.00883v1)

Abstract: Movement is how people interact with and affect their environment. For realistic character animation, it is necessary to synthesize such interactions between virtual characters and their surroundings. Despite recent progress in character animation using machine learning, most systems focus on controlling an agent's movements in fairly simple and homogeneous environments, with limited interactions with other objects. Furthermore, many previous approaches that synthesize human-scene interactions require significant manual labeling of the training data. In contrast, we present a system that uses adversarial imitation learning and reinforcement learning to train physically-simulated characters that perform scene interaction tasks in a natural and life-like manner. Our method learns scene interaction behaviors from large unstructured motion datasets, without manual annotation of the motion data. These scene interactions are learned using an adversarial discriminator that evaluates the realism of a motion within the context of a scene. The key novelty involves conditioning both the discriminator and the policy networks on scene context. We demonstrate the effectiveness of our approach through three challenging scene interaction tasks: carrying, sitting, and lying down, which require coordination of a character's movements in relation to objects in the environment. Our policies learn to seamlessly transition between different behaviors like idling, walking, and sitting. By randomizing the properties of the objects and their placements during training, our method is able to generalize beyond the objects and scenarios depicted in the training dataset, producing natural character-scene interactions for a wide variety of object shapes and placements. The approach takes physics-based character motion generation a step closer to broad applicability.

An Essay on "Synthesizing Physical Character-Scene Interactions"

In the paper "Synthesizing Physical Character-Scene Interactions," the authors address the challenge of generating realistic interactions between virtual characters and their environments, a fundamental problem in computer graphics and animation. The work introduces a method leveraging adversarial imitation learning and reinforcement learning to develop physically-simulated characters capable of performing scene interaction tasks naturally. This method effectively tackles two primary issues observed in existing systems: the simplicity of environments in character animations and the manual effort required for training data labeling.

The proposed system stands out by synthesizing interactions using large unstructured motion datasets without manual annotation. A key innovation is the conditioning of both the adversarial discriminator and the policy networks on the scene's context. This ensures that character movements are not only realistic but also contextually appropriate within a scene. The paper explores diverse scene interaction tasks such as carrying, sitting, and lying down, each of which requires interactive coordination of a character's movement concerning objects in the scene.

Physically simulated motion generation has been a persistent pursuit, with reinforcement learning (RL) frameworks recently gaining prominence for training control policies. However, achieving high-quality and natural motions using RL typically involves complex objective designs—an area addressed by this work through the concept of Adversarial Motion Priors (AMP). AMP serves to imitate behaviors from datasets without requiring explicit motion planners or detailed clip tracking, thus permitting versatile adaptations and transitions between behaviors critical for scene interaction.

The paper demonstrates impressive results through its policies, evidenced by seamless transitions between various character states like idling, walking, and task-specific gestures like sitting. The policy is robust under randomizations like object property variations and placements, which were integral to training generalizable interactions that extend application across diverse object configurations. The results were compared against prior kinematic and physics-based methods, indicating superior task performance and interaction realism.

From an evaluative perspective, the scene-specific conditioning of the adversarial discriminator marks a significant advancement. This approach leverages context to guide motion directivity logically, offering a more versatile and scalable solution compared to prior works reliant on static datasets or single-object focuses. Moreover, by randomizing object parameters during training, the system could generalize to various unseen objects during testing—a crucial advancement towards broad applicability in gaming, simulation, and virtual reality.

The method's implications are noteworthy both theoretically and practically. It suggests a shift towards unifying adversarial and reinforcement learning paradigms to handle multidimensional character interaction tasks without dense label dependency. Future development may involve expanding this approach to handle multi-task RL efficiently, advanced scene complexity, and dynamic, changing scenarios—contexts where traditional kinematic models falter.

Overall, "Synthesizing Physical Character-Scene Interactions" presents a compelling advancement in synthesizing realistic interactions within virtual environments. It challenges existing methodologies, offering a robust solution capable of adapting articulated character motions in complex scenes, paving the way for continued exploration in intelligent, life-like virtual characters.