ELSA3D用一个弹性锚定机制把3D理解和生成统一了，三个任务都刷了SOTA，还省了一半算力，想做3D的可以看看这篇
ELSA3D是一种统一3D模型，通过弹性语义锚定将文本和几何推理在匹配的抽象尺度上联合结构化。它使用尺度感知的八叉树分词器表示几何，并引入锚点令牌（Anchor Tokens）实现稀疏但精确的跨模态交互。在图像到3D生成、文本到3D生成和3D描述三项任务上，ELSA3D均取得最先进性能，同时相比其非弹性版本将FLOPs和推理延迟降低约一半。
ELSA3D: Elastic Semantic Anchoring for Unified 3D Understanding and Generation
Unified 3D foundation models aspire to generate 3D assets and reason about them in language within a single backbone, but their text-3D interaction remains largely implicit. Existing methods concatenate text and 3D tokens into a flat sequence and rely on self-attention, collapsing coarse structural cues and fine geometric details into one undifferentiated representation. We introduce ELSA3D, a unified 3D model that addresses this with elastic semantic anchoring, structuring language and geometric reasoning jointly along matched abstraction scales. ELSA3D represents geometry with a scale-aware octree tokenizer and introduces Anchor Tokens, sparse cross-modal units that select semantic cues, route them to the most relevant 3D scale, retrieve scale-specific geometric evidence, and write the fused signal back into the unified representation, keeping interaction sparse yet precise. A lightweight per-block router makes both computation and reasoning elastic, choosing which text tokens instantiate anchors at which geometric scale so that cross-modal capacity concentrates where alignment is most needed. ELSA3D 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.