Capturing the full plenoptic light transport across spatial, angular, and temporal dimensions has long been a pursuit in computational imaging, yet it remains fundamentally constrained by the high dimensionality of the sampling space and the physical inaccessibility of scene regions due to self-occlusions. While time-resolved imaging records the temporal axis, existing methods are bottlenecked by the combinatorial complexity of the plenoptic function. This high dimensionality makes dense omni-dimensional sampling physically prohibitive. Simultaneously, tight coupling between illumination and viewpoint in current systems also precludes the full acquisition of plenoptic light transport. In this work, we present GenPIE, a Generative Plenoptic Imager designed to bridge the gap between sparse physical observations and high-dimensional light transport. We introduce a decoupled laser-detector hardware setup that enables independent control over illumination and detection, allowing for active probing of indirect light paths. To overcome the ill-posedness of sparse sampling and physical blind spots, we propose a generative inverse transient rendering framework. Our approach leverages 3D foundation models to provide strong semantic and 3D geometric priors for initialization, which are subsequently refined through a differentiable transient path tracer to ensure physically grounded adherence to the Transient Rendering Equation. We demonstrate that GenPIE supports a range of applications that are challenging for steady-state or purely neural methods, including disentangling multi-bounce light transport directly from captured transient videos, time unwarping, and time-resolved relighting. The project page is at https://wangzh1.github.io/GenPIE.

Recent advancements in autoregressive transformers have demonstrated remarkable potential for generating artist-quality meshes. However, the token ordering strategies employed by existing methods typically fail to meet professional artist standards, where coordinate-based sorting yields inefficiently long sequences, and patch-based heuristics disrupt the continuous edge flow and structural regularity essential for high-quality modeling. To address these limitations, we propose Strips as Tokens ( SATO ), a novel framework with a token ordering strategy inspired by triangle strips. By constructing the sequence as a connected chain of faces that explicitly encodes UV boundaries, our method naturally preserves the organized edge flow and semantic layout characteristic of artist-created meshes. A key advantage of this formulation is its unified representation, enabling the same token sequence to be decoded into either a triangle or quadrilateral mesh. This flexibility facilitates joint training on both data types: large-scale triangle data provides fundamental structural priors, while high-quality quad data enhances the geometric regularity of the outputs. Extensive experiments demonstrate that SATO consistently outperforms prior methods in terms of geometric quality, structural coherence, and UV segmentation.