Sequence models have demonstrated remarkable success in behavioral planning by leveraging previously collected demonstrations. However, solving multi-task missions remains a significant challenge, particularly when the planner must adapt to unseen constraints and tasks, such as discovering goals and unlocking doors. Such behavioral planning problems are challenging to solve due to: a) agents failing to adapt beyond the single task learned through their reward function, and b) inability to generalize to new environments, e.g., those with walls and locked doors, when trained only in planar environments. Consequently, state-of-the-art decision-making methods are limited to missions where the required tasks are well-represented in the training demonstrations and can be solved within a short (temporal) planning horizon. To address this, we propose GenPlan: a stochastic and adaptive planner that leverages discrete-flow models for generative sequence modeling, enabling sample-efficient exploration and exploitation. This framework relies on an iterative denoising procedure to generate a sequence of goals and actions. This approach captures multi-modal action distributions and facilitates goal and task discovery, thereby generalizing to out-of-distribution tasks and environments, i.e., missions not part of the training data. We demonstrate the effectiveness of our method through multiple simulation environments. Notably, GenPlan outperforms state-of-the-art methods by over 10% on adaptive planning tasks, where the agent adapts to multi-task missions while leveraging demonstrations from single-goal-reaching tasks.

Reconstructing realistic human avatars from monocular videos is a challenge that demands intricate modeling of 3D surface and articulation. In this paper, we introduce a comprehensive approach that synergizes three pivotal components: (1) a Signed Distance Field (SDF) representation with volume rendering and grid-based ray sampling to prune empty raysets, enabling efficient 3D reconstruction; (2) faster 3D surface reconstruction through a warmup stage for human surfaces, which ensures detailed modeling of body limbs; and (3) temporally consistent subject-specific forward canonical skinning, which helps in retaining correspondences across frames, all of which can be trained in an end-to-end fashion under 15 minutes.Leveraging warmup and grid-based ray marching, along with a faster voxel-based correspondence search, our model streamlines the computational demands of the problem. We further experiment with different sampling representations to improve ray radiance approximations and obtain a floater free surface. Through rigorous evaluation, we demonstrate that our method is on par with current techniques while offering novel insights and avenues for future research in 3D avatar modeling. This work showcases a fast and robust solution for both surface modeling and novel-view animation. Project website: https://aku02.github.io/projects/avatarone