TACC: Target-Aware Carrier Compression for Dense-Context Novel View Synthesis

Recent feed-forward approaches mark a paradigm shift in novel view synthesis by eliminating the reliance on per-scene optimization, directly inferring 3D representations from input views. However, their reliance on global scene-token context incurs substantial memory and latency overhead, constraining their ability to handle increasingly dense source-view contexts. Compressing source representations offers an intuitive way to limit this token budget. Prior general-purpose compression methods are not tailored for novel view synthesis, as their fixed, target-agnostic nature fails to adapt evidence allocation to individual target views. To overcome this, we introduce TACC, a target-aware compression module tailored for feed-forward latent NVS. Specifically, TACC organizes each source view into a progressive carrier hierarchy. Driven by source-target geometric relevance, it adaptively retrieves tokens to dynamically allocate exact computational quotas. This selective retrieval directly yields a compact, target-conditioned representation. Extensive experiments on RE10K demonstrate that, under dense-input settings, TACC accelerates inference by 9.23× over LagerNVS and 64× over LVSM.