STANCE: Controllable Video Generation for Structured Dynamics via Sparse-To-dense ANChored Encoding

Video generation has recently made striking visual progress,but maintaining coherent object motion and interactions remains dif-ficult. We trace two practical bottlenecks: (i) human-provided motionhints (e.g., small 2D maps) often collapse to too few effective tokens afterencoding, weakening guidance; and (ii) Current architectures inevitablybias toward pixel-level texture reconstruction over complex structuralmotion, yielding videos that are photorealistic but physically incoherent.We present STANCE, an image-to-video framework that addresses bothissues with two simple components. First, we introduce Instance Cues,a pixel-aligned control signal that turns sparse, user-editable hints into adense 2.5D (camera-relative) motion field by averaging per-instance flowand augmenting with monocular depth over the instance mask. Thisreduces depth ambiguity compared to 2D drag/arrow inputs while re-maining easy to user. Second, we preserve the salience of these cuesin the latent token space with Dense RoPE, which tags a small setof initial-frame motion tokens with spatially-addressable rotary embed-dings. Finally, rather than treating appearance and structure as looselycoupled tasks, STANCE integrates an auxiliary stream directly withinthe same Diffusion Transformer (DiT). By sharing identical latent tokensand Dense RoPE, this auxiliary stream acts as a "geometry witness" thatheavily penalizes geometric drift.