# Text-to-3D Generation

Digitizing a plant in 3D usually takes tens to hundreds of calibrated photographs, or costly laser scanning and robotic capture, while single-image generators struggle to align their output back to the input photo. Plant Orbiter uses a video diffusion model to synthesize an orbit video around the plant from one frontal image, then fits 3D Gaussian splats using the known camera path, skipping structure-from-motion camera recovery. Across PlantDreamer, Botany-Bot and PLANesT, its 3D F-score leads the compared methods on every tested dataset, its input-view IoU reaches 0.552 and 0.556 on PlantDreamer and Botany-Bot, and height measured on the GrowSplat time series gives a mean absolute percentage error of 5.9%.

Digitizing a plant in 3D usually takes tens to hundreds of calibrated photographs, or costly laser scanning and robotic capture, while single-image generators struggle to align their output back to the input photo. Plant Orbiter uses a video diffusion model to synthesize an orbit video around the plant from one frontal image, then fits 3D Gaussian splats using the known camera path, skipping structure-from-motion camera recovery. Across PlantDreamer, Botany-Bot and PLANesT, its 3D F-score leads the compared methods on every tested dataset, its input-view IoU reaches 0.552 and 0.556 on PlantDreamer and Botany-Bot, and height measured on the GrowSplat time series gives a mean absolute percentage error of 5.9%.

ELSA3D introduces elastic semantic anchoring, representing geometry with a scale-aware octree tokenizer and adding sparse cross-modal Anchor Tokens that select semantic cues, route them to the most relevant 3D scale, and write the fused signal back into the unified representation, with a lightweight per-block router making computation and reasoning elastic; it achieves state-of-the-art performance across image-to-3D generation, text-to-3D generation, and 3D captioning, outperforming the strongest unified baseline while roughly halving FLOPs and inference latency relative to the non-elastic version of the same model.

ELSA3D introduces elastic semantic anchoring, representing geometry with a scale-aware octree tokenizer and adding sparse cross-modal Anchor Tokens that select semantic cues, route them to the most relevant 3D scale, and write the fused signal back into the unified representation, with a lightweight per-block router making computation and reasoning elastic; it achieves state-of-the-art performance across image-to-3D generation, text-to-3D generation, and 3D captioning, outperforming the strongest unified baseline while roughly halving FLOPs and inference latency relative to the non-elastic version of the same model.