{"id":"e4ef09fa-4d41-4b07-94e9-21c68d66e249","arxiv_id":"2507.13929","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"A generalizable NeRF that renders novel views of unseen scenes at arbitrary viewpoints and times of day from few input views, without per-scene optimization.","lead":"TimeNeRF is a neural rendering system that builds a 3D scene from just two or three photos and can render that scene from new viewpoints at different times of day, without retraining for each new scene. It combines multi-view stereo, NeRF, and image style transfer so that day-to-night appearance changes happen in 3D instead of being painted on flat 2D images.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The temporal axis is ungrounded: t is learned only from DRIT++ pseudo targets and no experiment compares rendered views to real frames at known times, so the 'arbitrary time' claim may be an artifact of the same model that supplies the supervision.","rationale":"The reader's CONDITIONAL verdict is appropriate. The static few-shot NVS claims (Table 2) are benchmarked against MVSNeRF and GeoNeRF, and the architecture is well-structured. However, the paper's distinctive contribution—arbitrary-time rendering—depends on a disentanglement and a time code that are validated only through the same DRIT++ model used to produce supervision. The supplementary material even shows residual leakage in original DRIT++ (Fig. 13) and that L_t0 and L_dt are needed to prevent degenerate behavior (Fig. 5), confirming the mechanism is delicate. None of the reported metrics uses ground-truth images at known timestamps, so the claim that t corresponds to real time-of-day is untested. The proposed end-to-end evaluation with known timestamps would settle this. I agree with the reader's weakest-assumption diagnosis; I would keep the verdict CONDITIONAL and require code, error bars, and that temporal ground-truth evaluation.","tokens_in":26245,"tokens_out":8484,"duration_ms":106665,"concrete_test":"Run TimeNeRF on a held-out scene with true time-of-day ground truth (e.g., a multi-view camera rig recording a static outdoor scene over 24 hours, or a synthetic scene with known sun position). Supply two views from time t0, query t_target, and compute PSNR/LPIPS between the rendered novel view and the actual ground-truth image at t_target, for target times spanning dawn, day, dusk, and night. This single end-to-end test settles whether the t code maps to real time-of-day; chance-level or t-invariant errors would indicate the temporal contribution is an artifact of the DRIT++ pseudo-target manifold.","verdict_should_be":"UNCHANGED","load_bearing_attack":"TimeNeRF's central claim is that querying t yields a true time-of-day appearance. Supporting this requires two things: (i) the DRIT++ content features used in Sections 3.3-3.4 are invariant to illumination and weather, so geometry and density are time-independent; and (ii) the cyclic time code t, predicted by g_t from style features in Section 3.5, is calibrated to real times of day. Neither is established. Stage 2 trains exclusively with L_style_mse (Eq. 8), whose pseudo ground truth is the style-transferred output of DRIT++—the same model that produced the content extractor. There are no time labels (Section 3.5: 'train the model without relying on exact time data'). The only temporal quantifications are FID and YCbCr histogram correlations against reference styles (Section 6.5.2, Tables 4-5, Fig. 17) plus a subjective user preference study (Section 4.3); none measures whether a rendered image at t matches an actual capture of that scene at t. If DRIT++'s style space does not correspond to true clock time, or if content leakage perturbs the cost volume, the smooth cycle in [0,2π) is a property of the pseudo targets, not of real day-to-night transitions. This is a missing-validation gap in the central contribution, not a disagreement with community consensus.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"TimeNeRF proposes a few-shot, generalizable neural radiance field that also models time-of-day appearance changes. The method first trains a modified DRIT++ to disentangle content from style, then builds a content radiance field from multi-level content features and cost volumes, and finally converts it into a time-dependent radiance field by injecting a time code t in [0,2π) together with time-irrelevant features. Training uses an MSE loss, a pseudo stylized loss against DRIT++ stylized targets, a Δt cyclic-consistency loss, and a t0 reconstruction loss. Experiments report static few-shot novel-view-synthesis results comparable to GeoNeRF/MVSNeRF, qualitative day-to-night transitions, cross-view consistency metrics, ablations, and a user study, with additional FID and YCbCr histogram analyses in the supplementary material.","tokens_in":26591,"tokens_out":5939,"duration_ms":68789,"significance":"If the temporal claim holds, the paper would be a meaningful first step toward few-shot, generalizable novel-view synthesis with an explicit time axis and no per-scene optimization. The static few-shot results on Ithaca365 and LLFF are benchmarked against credible baselines and appear internally consistent. However, the central temporal contribution is currently validated only against pseudo stylized targets produced by the same model that defines the time code, and none of the quantitative metrics compares a rendered view at a specified t with an actual capture of that scene at that time. The contribution is therefore defensible but needs load-bearing additional validation before the arbitrary-time claim can be accepted.","major_comments":[{"comment":"The central claim that a user can query t and obtain a render at the true time of day is not validated against any real time-of-day ground truth. The pseudo stylized loss L_style_mse (Eq. 8) is computed against C_pseudo(r) generated by the modified DRIT++ from a reference image, and the same reference style feature is mapped by g_t to the t used in rendering. The experiments in Sec. 6.5.2 (FID, YCbCr histogram correlation) and the user study in Sec. 4.3 compare against reference styles or subjective preference; none checks whether a render at t matches an actual photograph of that scene at that time. Since L_style_mse can be minimized by reproducing the DRIT++ style manifold, the smooth cycle in [0,2π) may be an artifact of the pseudo targets rather than a model of real day-to-night transitions. Please add a direct validation: use time-stamped captures, for example the per-frame time-of-day metadata in Waymo and the repeated traversals in Ithaca365, to show that (i) the ordering and spacing of g_t predictions are consistent with real capture times, and (ii) for held-out scenes, a render at t is closer to the real capture at t than to captures at other times.","section":"Sec. 3.5, Eq. (8), Sec. 6.5.2"},{"comment":"The architecture's load-bearing premise is that DRIT++ content features are invariant to environmental change factors. This premise is never directly tested; Sec. 3.3 asserts that extraction 'excludes environmental change factors,' while Supp. Sec. 5.2.2 states only that 'we hypothesize that the extracted style encompasses both time and weather information.' If illumination or weather leaks into content features, the cost volume and the predicted density in Eqs. (3)-(5) are time-contaminated, and the later time code cannot cleanly control appearance. The modified-DRIT++ ablation in Fig. 13 actually shows residual daytime information in the original DRIT++ content features, which makes the risk concrete. I ask for a quantitative invariance test, such as comparing predicted depth or geometry features for the same static scene under the same viewpoint across different times and weather conditions in Ithaca365, to show that the geometry representation is stable across times while colors change with t.","section":"Sec. 3.3-3.4, Supp. Sec. 5.2.2"},{"comment":"The Delta_t loss (Eq. 10) uses colors generated from random samples t and t' in [0,2π) and compares the predicted color difference with the code-space distance |t-t'|. This enforces cyclic consistency in the latent time code but does not anchor the code to clock time; without calibration, small |t-t'| in the code does not correspond to small temporal separation in the real world. This is a consequence of the missing calibration in the first major comment, and the loss should be re-evaluated once g_t is tested against real time labels.","section":"Sec. 3.7, Eq. (10)"}],"minor_comments":[{"comment":"The time labels in Fig. 3 (t = 0, t = π, etc.) are code coordinates in [0,2π), not real clock times; the caption and text should say this explicitly to avoid overclaiming continuous real-time control.","section":"Sec. 4.3, Fig. 3"},{"comment":"The 'real_imgs' baseline in Fig. 17 is mentioned in the caption but never defined in the text; specify which 24-hour videos were used and how the ground-truth time placement was obtained.","section":"Fig. 17 and Tables 4-5"},{"comment":"The statement that the datasets do not contain time labels is imprecise, since both Waymo and Ithaca365 provide per-frame time-of-day or traversal metadata; use 'exact continuous timestamps' instead of 'time labels'.","section":"Sec. 3.2 and Sec. 4.1"},{"comment":"The sentence 'we utilize MVSnet as our pretrained model for feature extraction' conflicts with the description in Sec. 3.3 of a 3D-UNet applied to cost volumes; clarify which modules are initialized from MVSNet.","section":"Supp. Sec. 5.2.3"},{"comment":"The implementation section states that code will be available but gives no repository link or release mechanism; provide a public link or explicit availability statement for reproducibility.","section":"Supp. Sec. 7"},{"comment":"The caption contains the typo 'Itheca365'; correct it to 'Ithaca365'.","section":"Table 5"}],"recommendation":"major_revision","confidential_remarks":"The static few-shot contribution is sound and reasonably benchmarked, but the temporal axis is the paper's headline claim and is currently supported only by self-referential supervision. The authors state that the datasets lack time labels, yet public metadata for Waymo and Ithaca365 provides at least coarse time-of-day information; using that metadata for a validation experiment would substantially strengthen the paper. I would be willing to reconsider after such an experiment is added."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: the static few-shot NVS story is credible; the temporal story is plausible but under-validated. The paper's genuine novelty is the combination of a GeoNeRF-style generalizable radiance field with DRIT++-style disentanglement and an explicit time code, letting a model render novel views of unseen scenes from two or three inputs without per-scene optimization, then re-render them at user-specified times. On Ithaca365 and LLFF, TimeNeRF is essentially at parity with GeoNeRF (27.67 vs 27.46 PSNR on Ithaca365; 19.71 vs 19.62 on LLFF), which is a fair result for a model that also carries a time branch. The cross-view consistency numbers against 2D translation baselines are also reasonable and point to a real advantage of doing time modulation in 3D. The related work is placed correctly and the citation pattern looks fair.\n\nWhere I agree with the stress-test note is that the central temporal claim is not properly tested. Stage 2 supervises time with the pseudo stylized loss, whose pseudo ground truth is generated by the same DRIT++ model that produced the content extractor. The time code t is learned as a mapping from style features into [0,2π) without time labels, so a user-specified t is not calibrated to clock time. No experiment shows that a rendered frame at time t matches an actual photograph of that scene at that time. The supplementary does include YCbCr histogram correlations against real timelapse frames (Fig. 17), which is a partial external anchor for the day/night trend, but it is a global color-statistics check, not a scene-level comparison, so it does not close the gap. The absence of released code or pretrained weights makes independent verification hard.\n\nI would not call the paper incoherent. The architecture is sensible, the ablations show the cyclic losses and two-branch design do something, and the authors openly state they train without exact time data. The problem is a validation gap in the headline contribution, not a logical flaw. The claimed capability may well work; it just is not demonstrated yet.\n\nThis paper is for anyone working on generalizable few-shot NVS or time/weather control in neural rendering. It deserves a serious referee, but I would not accept it as-is. I would request code and, more importantly, an experiment with real time-labeled multi-view captures or at least per-scene alignment between rendered outputs and images at known times, plus error bars. Without that, the time axis should be described as stylized appearance interpolation rather than true time-of-day synthesis.","headline":"Static few-shot NVS is solid; the temporal claim is plausible but rests on self-referential pseudo supervision with no real time-of-day validation.","tokens_in":27122,"tokens_out":3251,"would_cite":true,"duration_ms":39075,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"TimeNeRF renders novel views of unseen scenes at any time of day, from just a few input images and without per-scene retraining.","keywords":["neural radiance fields","few-shot novel view synthesis","generalizable NeRF","time-of-day rendering","content-style disentanglement","continuous image translation","day-night transition","cost volume"],"falsifier":"A direct test would be to take the same static scene photographed at several distinct times of day (ideally with aligned cameras), feed each time's views into TimeNeRF, and compare the predicted densities and the geometry features at corresponding 3D locations; if the content radiance field is truly time-invariant, the densities should be nearly identical across capture times, whereas any systematic disagreement shows that environmental factors leaked into the content features and the time code is compensating for improper geometry.","tokens_in":26060,"feed_emoji":"🌆","tokens_out":3868,"duration_ms":33723,"temperature":0.7,"pith_summary":"TimeNeRF aims to solve a problem no prior NeRF-style method has directly addressed: synthesizing novel viewpoints of an unseen 3D scene at arbitrary times of day, not just at the times when photos were taken. The paper's central claim is that a single model can learn a continuous function $(c_t,\\sigma) = F_\\phi(f_x, d, t)$ that maps a 3D point's content features, viewing direction, and a time code to a time-dependent color and a shared density, so that volume rendering produces new views with smooth day-to-night transitions. The intended payoff is practical: applications such as virtual worlds and the metaverse could change the lighting and atmosphere of a captured scene without recollecting data or re-optimizing a network per scene. The paper reports that TimeNeRF outperforms sequential combinations of view synthesis and image translation in cross-view consistency, and that it matches or exceeds few-shot generalizable baselines like MVSNeRF and GeoNeRF on pure novel view synthesis.","feed_headline":"TimeNeRF renders any scene at any time of day","feed_subtitle":"A few input photos are enough to synthesize new views with smooth dawn-to-dusk changes, no per-scene retraining.","key_machinery":"The framework is a two-stage pipeline. Stage 1 trains a modified DRIT++ image translation model to produce content features and style features, where content is meant to be free of environmental change factors. Stage 2 uses: (a) an appearance-agnostic geometry extractor that builds multi-level cost volumes from the content features and passes them through a 3D U-Net to get geometry features; (b) an implicit scene network based on GeoNeRF-style multi-head self-attention that predicts density $\\sigma$ and a 3D content feature $\\xi_x$ for each sample point; (c) a factors extraction module with two MLPs, $g_t$ mapping a reference style feature to a time in $[0,2\\pi)$ and $g_a$ extracting time-irrelevant features $a$ from the input views; and (d) a two-branch time-dependent radiance field constructor $T(\\xi_x, a, t)$ that fuses content features with time (branch 1) and with time plus $a$ (branch 2) to produce the color $c^x_t$. Four losses drive training: an MSE loss using view-weighted input colors to learn density, a pseudo stylized loss comparing rendered colors to DRIT++ stylized pseudo ground truth, a $\\Delta t$ loss that enforces unique outputs at different times via a small discriminator MLP, and a reconstruction loss at the predicted input time $t_0$ so the rendered view matches the original input style.","core_discovery":"The paper claims that a generalizable, few-shot neural radiance field can be made time-aware by first building a content radiance field from which environmental change factors (time of day, weather, illumination) have been removed, and then transforming it into the radiance field of a specific moment using a time code and time-irrelevant style factors. The key architectural claim is that the cost volume used for geometry estimation is constructed from disentangled content features instead of raw image features, which lets the same model handle input views captured under varying conditions. The time axis is encoded cyclically as $t \\in [0, 2\\pi)$ with $(\\cos t, \\sin t)$ embedding, so the model can be queried at arbitrary times during testing without any reference image.","pith_inferences":["The paper's architecture implies a testable decoupling: because the content radiance field and density are supposed to be time-invariant, the same geometry should be reconstructed from input views at different times of day for the same scene; a direct experiment comparing density or geometry outputs across capture times would validate or refute this decoupling more cleanly than the image-level abl","The $(\\cos t, \\sin t)$ embedding and the cyclic $\\Delta t$ loss suggest the approach could be extended to other cyclic scene variations such as weather cycles, seasonal lighting, or even tidal or traffic patterns, by swapping the reference dataset used to supervise the time axis.","The reliance on DRIT++ for both content disentanglement and pseudo ground truth generation means the method's upper bound is set by how well that pretrained model disentangles; a future variant that replaces the self-referential supervision with real time-lapse photographs of the same viewpoint could raise that ceiling and give a more direct test of the core assumption."],"forward_implications":["If the central claim holds, capturing a handful of photos of an outdoor scene under whatever conditions are available would be enough to later render that scene at any requested time of day, enabling cinematic and metaverse-style relighting without recollecting data.","Because the time code is a free input at test time, the model could drive continuous day-night animation from a static set of views, which is exactly the smooth dawn-to-dusk transition the paper demonstrates qualitatively.","Training on varied weather (Ithaca365) and varied time (Waymo) suggests that time and weather can be treated as separable axes, so future models could control them independently and generalize to unseen capture conditions.","The reported parity with GeoNeRF on standard few-shot view synthesis implies that adding a time axis does not have to come at the cost of plain novel view quality."],"supporting_citations":[{"why":"Defines the NeRF formulation (MLP mapping position and direction to color and density, with volume rendering) that TimeNeRF modifies.","marker":"[35]"},{"why":"GeoNeRF supplies the geometry-prior aggregation (multi-head self-attention over interpolated features) that the implicit scene network is adapted from.","marker":"[19]"},{"why":"DRIT++ provides the pretrained content/style disentanglement used in Stage 1, including the content features and style features that the whole time-disentanglement pipeline depends on.","marker":"[26]"},{"why":"Ithaca365 provides the multi-view, varied-weather training scenes used to build the content radiance field and to evaluate generalization.","marker":"[9]"},{"why":"Waymo Open Dataset provides the reference images spanning day, dusk, dawn, and night that teach the model the time axis.","marker":"[51]"},{"why":"IBRNet motivates removing the inter-sample distance $\\delta_i$ and using interpolated multi-view features for generalizable NeRF, which TimeNeRF adopts.","marker":"[54]"},{"why":"CoMoGAN is the continuous day-night image translation baseline that TimeNeRF is compared against and that motivates modeling cyclic time.","marker":"[43]"},{"why":"Cascade cost volume construction is the multi-view stereo technique that the appearance-agnostic geometry extractor builds upon.","marker":"[11]"},{"why":"MVSNet supplies the cost-volume-based depth inference approach and the pretrained feature extractor used by the geometry branch.","marker":"[62]"},{"why":"MVSNeRF is the few-shot generalizable NeRF baseline that TimeNeRF extends and compares against in the novel view synthesis experiments.","marker":"[2]"}],"fun_headline_variants":["Any time, any view: TimeNeRF from few shots","Few shots, any time: TimeNeRF does dawn-to-dusk","TimeNeRF: temporal NeRF from a handful of views","Day to night, few shots: TimeNeRF generalizes","From a few photos, TimeNeRF renders day or night"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The whole method depends on the pretrained DRIT++ content extractor returning content features that are truly free of environmental change factors, so that geometry and density are time-invariant; this assumption is never tested directly, and the pseudo stylized training labels are generated by the same DRIT++ model, making the disentanglement only indirectly and self-referentially validated.","fun_headline_variants_meta":{"raw":{"variants":["Any time, any view: TimeNeRF from few shots","Few shots, any time: TimeNeRF does dawn-to-dusk","TimeNeRF: temporal NeRF from a handful of views","Day to night, few shots: TimeNeRF generalizes","From a few photos, TimeNeRF renders day or night"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001696,"raw_usage":{"total_tokens":6722,"prompt_tokens":950,"completion_tokens":5772,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":566,"completion_tokens_details":{"reasoning_tokens":5684}},"tokens_in":566,"tokens_out":5772,"duration_ms":45306,"temperature":1.0,"reasoning_tokens":5684,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T16:14:19.096777+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A direct test would be to take the same static scene photographed at several distinct times of day (ideally with aligned cameras), feed each time's views into TimeNeRF, and compare the predicted densities and the geometry features at corresponding 3D locations; if the content radiance field is truly time-invariant, the densities should be nearly identical across capture times, whereas any systematic disagreement shows that environmental factors leaked into the content features and the time code is compensating for improper geometry.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Defines the NeRF formulation (MLP mapping position and direction to color and density, with volume rendering) that TimeNeRF modifies."},{"cited_title":"Johari, Y","cited_arxiv_id":null,"evidence_quote":"GeoNeRF supplies the geometry-prior aggregation (multi-head self-attention over interpolated features) that the implicit scene network is adapted from."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"DRIT++ provides the pretrained content/style disentanglement used in Stage 1, including the content features and style features that the whole time-disentanglement pipeline depends on."},{"cited_title":"Diaz-Ruiz, Youya Xia, Yurong You, Jose Nino, Junan Chen, Josephine Monica, Xiangyu Chen, Katie Luo, Yan Wang, Marc Emond, Wei-Lun Chao, Bharath Hariharan, Kilian Q","cited_arxiv_id":null,"evidence_quote":"Ithaca365 provides the multi-view, varied-weather training scenes used to build the content radiance field and to evaluate generalization."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Waymo Open Dataset provides the reference images spanning day, dusk, dawn, and night that teach the model the time axis."},{"cited_title":"Barron, Ricardo Martin-Brualla, Noah Snavely, and Thomas Funkhouser","cited_arxiv_id":null,"evidence_quote":"IBRNet motivates removing the inter-sample distance $\\delta_i$ and using interpolated multi-view features for generalizable NeRF, which TimeNeRF adopts."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"CoMoGAN is the continuous day-night image translation baseline that TimeNeRF is compared against and that motivates modeling cyclic time."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"MVSNet supplies the cost-volume-based depth inference approach and the pretrained feature extractor used by the geometry branch."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"MVSNeRF is the few-shot generalizable NeRF baseline that TimeNeRF extends and compares against in the novel view synthesis experiments."}],"review_version":1}