Unified 3D foundation models aspire to generate 3D assets and reason about them in language within a single backbone, where one model can reconstruct an object from an image, generate one from text, describe its structure, and support downstream reasoning over geometry. Yet their text–3D interaction remains largely implicit: existing methods concatenate text and 3D tokens into a flat sequence and rely on self-attention to discover cross-modal correspondences, collapsing coarse structural cues and fine geometric detail into a single undifferentiated representation. What is missing is not merely a stronger hierarchical 3D representation, but a unified design that structures language and geometric reasoning jointly. ELSA3D addresses this with elastic semantic anchoring, enabling structured interaction between semantic cues and geometric content.

Scale-aware octree tokenization

Each 3D object is represented as a multiscale octree constructed from a canonicalized 128³ voxel grid, recursively subdivided to a maximum depth. The first three octree levels are fully populated to provide a stable global scaffold; beyond this coarse scaffold the octree is sparse, with nodes added only where they intersect surface geometry. Each node carries a structural bit indicating whether it is subdivided and a content token encoding its local geometry, quantized against a scale-specific codebook so that each vocabulary specializes in geometric primitives at its resolution. Every content token is further augmented with a learned positional embedding and a fixed, non-trainable scale tag, making geometric resolution recoverable from a token's embedding and available to the transformer. Within each depth, nodes are serialized in Morton (Z-order) to preserve spatial locality in the token sequence.

Scale-aware octree tokenization. ELSA3D's octree VQ-VAE encodes a voxelized 3D shape into multiscale structural bits and scale-specific content codes, then decodes them to reconstruct the shape. Nodes are organized by octree depth and serialized with Morton/Z-order to preserve spatial locality within each scale.

Anchor Tokens

Dense interaction between all semantic tokens and all 3D tokens is computationally wasteful and semantically noisy, since many words provide only global or contextual constraints while only a subset requires precise geometric grounding. Anchor Tokens form a sparse semantic–geometric interface that creates cross-modal interaction only where it is useful. At each block, every selected semantic token is used as a query to cross-attend over the 3D tokens at its routed scale, and the retrieved scale-specific evidence is fused with the semantic state to form a transient, block-local anchor.

Dynamic routing and elastic reasoning

Because input difficulty varies across examples and only a subset of semantic tokens requires precise geometric grounding, a lightweight per-block router makes reasoning elastic in both computation and grounding. From a mean-pooled block context it decides whether to execute the block and how much MLP width to allocate, discretized into four levels. At the token level, it predicts for each semantic token an anchor gate — whether the token instantiates an anchor — and a soft distribution over geometric scales. By first selecting a scale and then attending within it, each token performs a coarse-to-fine search for its most relevant geometric correspondence.