View-conditioned 3D generators such as SAM 3D, TRELLIS, and Hunyuan3D produce high-quality object reconstructions from a single view, but real-world visual observation often arrives as long monocular streams. Naively applying these generators to each streaming frame independently leads to severe temporal inconsistency in the generated results. To address this problem, we propose Stream3D, the first training-free streaming mechanism that turns a frozen view-conditioned 3D generator into a streaming generator with constant cross-chunk memory. Stream3D achieves this by maintaining a compact evidential memory, which selectively caches the most informative historical frames based on a proposed evidence score mechanism. As the stream progresses, the memory dynamically updates to retain a fixed number of informative frames, preventing the memory footprint from growing linearly with sequence length. This also prevents degradation over long sequences and keeps the underlying generator completely unchanged without retraining, architectural modifications, or auxiliary losses. Evaluated on both realistic and synthetic streaming benchmarks, Stream3D outperforms latent-transport baselines, including KV-cache reuse and flow-based feature editing, across both photometric and geometric metrics. More details can be found at: https://stream-3d.github.io/stream3d.github.io/.

翻译：视点条件三维生成器（如SAM 3D、TRELLIS和Hunyuan3D）能够从单视角生成高质量的目标重建，然而真实世界的视觉观测通常以长单目流的形式呈现。若将这些生成器独立应用于每个流式帧，会导致生成结果出现严重的时序不一致性。为解决此问题，我们提出Stream3D——首个无需训练、基于恒定跨块记忆将冻结的视点条件三维生成器转化为流式生成器的机制。Stream3D通过维护紧凑的证据记忆实现该目标，该记忆基于所提出的证据评分机制选择性缓存最具信息量的历史帧。随着流数据推进，记忆动态更新以保留固定数量的信息帧，从而避免内存占用随序列长度线性增长。这同时防止了长序列中的性能退化，且无需重新训练、架构修改或辅助损失函数，保持底层生成器完全不变。在真实与合成流式基准测试上的评估显示，Stream3D在光度指标与几何指标上均优于包括KV缓存复用和基于流的特征编辑在内的潜在传输基线方法。更多详情请访问：https://stream-3d.github.io/stream3d.github.io/。