Fig. 2: GATOR architecture. (A) Pose-aware generative reconstruction. Following TRELLIS.2 (Xiang
et al., 2026), GATOR uses conditional flows for sparse structure, geometry, and appearance. RGB images and
depth-derived pointmaps retain scene context, while masks identify the target. Tokens from all three modalities
share a patch grid with common 2D position and Plücker-ray encodings. A local mixer exchanges information
at each patch within a view (Secs. 3.2 and 3.3). Cross-view attention and SigLIP2 object-name conditioning
guide structure generation. At its active coordinates, projected DINOv3 and NAF-upsampled features from
target-masked images are averaged across valid views and injected into every geometry and appearance de-
noising block. Global image tokens and T5Gemma2-encoded captions supply cross-attention. Shape latents
condition PBR material generation (Sec. 3.4). ❄ denotes frozen encoders. (B) Agentic refinement. Initialized
with the complete, scene-aligned asset, an agent uses input images and structural priors to complete parts, re-
pair topology, and refine texture coordinates and materials. Its edit-render-review loop compares original and
candidate renders under matched cameras and lighting to retain or revert local edits. Within the time budget, it
selects the best inspected asset while preserving reliable regions and scene-relative placement (Sec. 3.5).