cs.CVMar 24, 2026
SaveI3DM: Implicit 3D-aware Memory Retrieval and Injection for Consistent Video Scene Generation
Organizations: Monash University · Vertex Lab · Shanghai Jiao Tong University
Abstract
Despite remarkable progress in video generation, maintaining long-term scene consistency upon revisiting previously explored areas remains challenging. Existing solutions rely either on explicitly constructing 3D geometry, which suffers from error accumulation and scale ambiguity, or on naive camera Field-of-View (FoV) retrieval, which typically fails under complex occlusions. To overcome these limitations, we propose I3DM, a novel implicit 3D-aware memory mechanism for consistent video scene generation that bypasses explicit 3D reconstruction. At the core of our approach is a 3D-aware memory retrieval strategy, which leverages the intermediate features of a pre-trained Feed-Forward Novel View Synthesis (FF-NVS) model to score view relevance, enabling robust retrieval even in highly occluded scenarios. Furthermore, to fully utilize the retrieved historical frames, we introduce a 3D-aligned memory injection module. This module implicitly warps historical content to the target view and adaptively conditions the generation on reliable warping regions, leading to improved revisit consistency and accurate camera control. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches, achieving superior revisit consistency, generation fidelity, and camera control precision.
Figures & tables
Figure 2: Limitations of existing memory mechanisms. Top: Explicit geometry-based methods (e.g., Gen3C ( Ren et al. 2025 ) ) suffer from scale ambiguity, causing inaccurate camera navigation (e.g., colliding with the wall) and revisit inconsistencies. Bottom: Implicit FoV-based methods (e.g., WorldPlay ( Sun et al. 2025 ) ) fail under occlusions, as FoV overlap ignores actual visibility. Retrieving irrelevant historical frames causes repeated content and inconsistent revisits. Frames with matching colors denote the same viewpoint and should be strictly consistent. Red circles and boxes indicate inconsistencies and repeated content.
Figure 3: Overview of the proposed I3DM framework. Left: 3D-aware Memory Retrieval. For each historical frame in the memory bank, we first extract 3D-aware intermediate features using a pre-trained NVS model. A lightweight scoring network then evaluates their spatial relevance to the target view to select the most relevant frames. Right: 3D-aligned Memory Injection. The retrieved frames are processed by an Adaptive NVS Module to align them with the target view. These aligned results are then used to condition the Wan-DiT backbone for consistent video scene generation.
Figure 4: 3D-aware Memory Retrieval Module. We first set the last frame as an anchor. For each historical candidate frame, we extract implicit 3D-aware features from the layer of the frozen LVSM. Based on these features, a Scoring CNN then predicts a spatial uncertainty map for the target view. Notably, the full Transformer is executed only during training to provide supervision; during inference, the process terminates at the layer to efficiently deduce the score map.
| Method | Visual Quality | Camera Control | Revisit Consistency | ||||
|---|---|---|---|---|---|---|---|
| FID | FVD | PSNR | SSIM | LPIPS | |||
| Re10K Dataset | |||||||
| ViewCrafter | 58.63 | – | 71.116 | 0.7867 | 9.29 | 0.270 | 0.6797 |
| Voyager | 31.90 | 377.86 | 17.801 | 0.3471 | 14.75 | 0.499 | 0.4708 |
| Gen3C | 29.05 | 292.56 | 8.183 | 0.1450 | 15.23 | 0.553 | 0.4397 |
| WorldWarp | 21.90 | 181.95 | 13.003 | 0.2161 | 14.77 | 0.592 | 0.4828 |
Table 1: Comparisons on Re10K (top) and T&T (bottom).
| Method | FID | FVD | PSNR | SSIM | LPIPS | ||
|---|---|---|---|---|---|---|---|
| Temporal | 23.67 | 189.39 | 5.617 | 0.1277 | 13.78 | 0.522 | 0.4660 |
| Random | 18.50 | 141.88 | 2.075 | 0.0566 | 19.25 | 0.687 | 0.2027 |
| FoV-based | 17.63 | 134.69 | 1.895 | 0.0510 | 22.72 | 0.781 | 0.1084 |
| Geo-based | 17.79 | 133.66 | 2.027 | 0.0511 | 20.94 | 0.738 | 0.1346 |
| I3D-TopK | 17.59 | 134.13 | 2.046 | 0.0498 | 22.33 | 0.769 | 0.1138 |
| I3D-Ours | 17.55 | 131.66 | 1.991 | 0.0505 | 24.73 | 0.828 | 0.0756 |
Table 2: Ablation studies of memory retrievals (top) and memory injection mechanisms (bottom) on Re10K Dataset.
Figure 5: Qualitative comparison on the Re10K (left) and T&T (right) datasets. The frames with matching colors should remain strictly consistent; red circles and arrows highlight visual inconsistencies and inaccurate camera motion, respectively.
Figure 6: Ablation of memory retrieval strategies. Frames with the same color should remain strictly consistent. Red circles highlight visual inconsistencies. Temporal and random strategies fail to maintain scene consistency. Geometry-, FoV- and I3D-TopK-based approaches improve revisit consistency but still exhibit artifacts, while our full I3D-based retrieval strategy robustly maintains strict consistency.
Figure 7: Ablation of memory injection mechanisms. Removing memory or spatial alignment compromises revisit consistency, while freezing the NVS module causes inaccurate camera motion and navigation failures (e.g., failing to enter the room). Jointly fine-tuning the NVS module ensures both consistent generation and accurate camera control.
Appendix figures & tables11 assets
Supplementary material from the paper’s appendix.
Appendix
| Frames | Bank size | Retrieval time | Total time | Ratio |
|---|---|---|---|---|
| 77 | 4 | 0.00 | 1.24 | 0.0% |
| 229 | 44 | 0.04 | 1.32 | 3.1% |
| 457 | 104 | 0.10 | 1.36 | 7.3% |
| 609 | 144 | 0.14 | 1.39 | 9.9% |
| 837 | 204 | 0.19 | 1.47 | 13.2% |
| 989 | 244 | 0.23 | 1.50 | 15.5% |
Table 1: Per-frame runtime (sec) as generated video length increases. Bank size denotes the number of frames in the memory bank for retrieval before generating the current clip.
Figure 1: Our method alleviates temporal drifting by memory mechanism.
Figure 2: Visualization of confidence maps and greedy maximum-coverage selection. Given the anchor frame and target Plücker rays, our network predicts a spatial confidence map for each candidate (middle row). The selected candidates provide complementary marginal gains that collectively cover the target view (bottom row).
Figure 3: Visual comparison of the frozen and adaptive NVS modules. The purple dashed boxes indicate warping errors introduced by the frozen NVS module, which causes inaccurate camera motion in the generated results (highlighted by red dashed boxes). The adaptive NVS module suppresses these unreliable warped regions, allowing the model to fall back on its internal generative priors to produce convincing results with accurate camera motion.
| Layer | Visual Quality | Revisit Consistency | Per-clip Time Cost | ||||
|---|---|---|---|---|---|---|---|
| FID | FVD | PSNR | SSIM | LPIPS | Retrieval | Generation | |
| L0 | 18.655 | 148.764 | 17.183 | 0.634 | 0.2764 | 0.1853 | 56.7241 |
| L4 | 18.098 | 133.384 | 24.609 | 0.823 | 0.0786 | 1.3481 | 58.3389 |
| L6 | 17.553 | 131.657 | 24.732 | 0.828 | 0.0756 | 1.9739 | 59.0871 |
| L12 | 17.656 | 134.054 | 24.771 | 0.828 | 0.0761 | 3.8737 | 61.4180 |
| L24 | 17.599 | 128.981 | 24.823 | 0.830 | 0.0742 | 7.6167 | 64.9117 |
Table 2: Ablation studies of different feature layers for memory retrieval on the Re10K dataset. Time costs are averaged per video clip and reported in seconds (s).
Figure 4: Replacing the FoV-based retrieval of Matrix-Game-3.0 ( Wang et al. 2026 ) with our 3D-aware retrieval strategy improves revisit consistency while retaining its original frame-concatenation injection.
Figure 5: Qualitative comparison of different memory retrieval strategies. (Top) The camera trajectory illustrates the memory bank initialization (blue) and the target sequence to be generated (orange), alongside the ground truth (GT) frames. (Bottom) For each strategy, the left panel shows the four retrieved historical frames, while the right panel displays the corresponding target views synthesized by the pre-trained NVS model. Red dashed circles highlight artifacts caused by suboptimal retrieval.
Figure 6: Qualitative comparison on an outdoor RealEstate10K scene. Black dashed arrows link corresponding frames that should remain consistent; red dashed circles highlight visual inconsistencies, red dashed arrows indicate inaccurate camera motion, and red dashed boxes denote repetitive generated content.
Figure 7: Qualitative comparison on an indoor RealEstate10K scene. Black dashed arrows link corresponding frames that should remain consistent; red dashed circles highlight visual inconsistencies.
Figure 8: Qualitative comparison on the out-of-distribution scenes. Red dashed boxes denote the generated artifacts.
Figure 9: Our visual results on the RealEstate10K dataset. Black dashed arrows link corresponding frames that should remain consistent.