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[PENDING: G1]

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Thank you for the review. Tables, videos and details: https://memorizon-rebuttal.pages.dev/

Q1 (abstract notation). Fixed. The abstract now reads "bounded by k x K", and the preceding sentence is split for readability.

Q2 (real-world video). Training on real video needs camera poses. Metric scale is ambiguous in estimated poses, and pose noise changes which frames are retrieved (our retrieval uses pose only), so we keep real-video training as future work and say so in the paper.

We did add a zero-shot test on real capture (https://memorizon-rebuttal.pages.dev/e6/). It uses 20 handheld scenes from DL3DV-10K. Each model gets only the scene's first frame and its own camera path (translations rescaled to the training speed, intrinsics fixed at 90 degrees horizontal FOV) and generates 10-13 s. The 200 s and 400 s models never saw real video. The camera follows the prescribed path: direction agreement 0.93 / 0.90 and correlation with the prescribed yaw 0.79 / 0.73 (400 s / 200 s). Pixel agreement with the real frames is not better than repeating the first frame (PSNR 11.7 vs 12.0, LPIPS 0.58 vs 0.55 over the first 3 s), so we claim path following, not fidelity. The implied horizontal FOV (111-118 degrees against the 90 we assume) is unexplained. All 20 clips are on the page above.

Consistency versus quality. Table 13 attributes the quality loss to a train/test mismatch of the bank (clean at training, self-generated at inference), not to a weaker generator. [PENDING: G1] A milder bank-noise variant at 200 s tests whether this gap narrows: [fill in Revisit and quality against the plain 200 s model].

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Thank you for the review. Tables, videos and details: https://memorizon-rebuttal.pages.dev/

**Q1 (abstract notation).** Fixed. The abstract now reads "bounded by k x K", and the preceding sentence is split for readability.

**Q2 (real-world video).** Training on real video needs camera poses. Metric scale is ambiguous in estimated poses, and pose noise changes which frames are retrieved (our retrieval uses pose only), so we keep real-video training as future work and say so in the paper.

We did add a zero-shot test on real capture (https://memorizon-rebuttal.pages.dev/e6/). It uses 20 handheld scenes from DL3DV-10K. Each model gets only the scene's first frame and its own camera path (translations rescaled to the training speed, intrinsics fixed at 90 degrees horizontal FOV) and generates 10-13 s. The 200 s and 400 s models never saw real video. The camera follows the prescribed path: direction agreement 0.93 / 0.90 and correlation with the prescribed yaw 0.79 / 0.73 (400 s / 200 s). Pixel agreement with the real frames is not better than repeating the first frame (PSNR 11.7 vs 12.0, LPIPS 0.58 vs 0.55 over the first 3 s), so we claim path following, not fidelity. The implied horizontal FOV (111-118 degrees against the 90 we assume) is unexplained. All 20 clips are on the page above.

**Consistency versus quality.** Table 13 attributes the quality loss to a train/test mismatch of the bank (clean at training, self-generated at inference), not to a weaker generator. [PENDING: G1] A milder bank-noise variant at 200 s tests whether this gap narrows: [fill in Revisit and quality against the plain 200 s model].