As a central example, we highlight the case one agent is no-swap and the other's regret is

Procedural Content Generation (PCG) is a technique to generate complex and diverse environments in an automated way.

Infants possess a remarkable ability to rapidly learn and process visual inputs.

Visual In-Context Learning (VICL) is a prevailing way to transfer visual foundation models to new tasks by leveraging contextual information contained in in-context examples to enhance learning and prediction of query samples.

In this section, we provide detailed derivation for the kinematics proposed in the main paper. The numbers in () indicate the dimension of output features. S is a shape matrix that we set to the identity matrix I in LEP ARD since we use one-to-one mapping for the local deformation estimation. Finally, we obtain a pseudo ground-truth object silhouette G by thresholding the minimum feature distance to the center of the clusters. In Figure 1, we provide the architecture of the encoder-decoder model proposed in the main paper.

Reconstructing the 3D articulated shape of an animal from a single in-the-wild image is a challenging task.