EA3D: Event-Augmented 3D Diffusion for Generalizable Novel View Synthesis
Authors: Wangbo Yu
Chaoran Feng
Jianing Li
Aofan Zhang
Zhenyu Tang
Mingyi Guo
Wei Zhang
Zhengyu Ma
Li Yuan
Yonghong Tian
Venue: ICLR 2026 Poster (2026)
Keywords: Novel view synthesis; Event Cameras; Diffusion model
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: We introduce **EA3D**, an Event-Augmented 3D Diffusion framework for generalizable novel view synthesis from event streams and sparse RGB inputs.  Existing approaches either rely solely on RGB frames for generalizable synthesis, which limits their robustness under rapid camera motion, or require per-scene optimization to exploit event data, undermining scalability.
EA3D addresses these limitations by jointly leveraging the complementary strengths of asynchronous events and RGB imagery. 
At its core lies a learnable EA-Renderer, which constructs view-dependent 3D features within target camera frustums by fusing appearance cues from RGB frames with geometric structure extracted from adaptively sliced event voxels. 
These features condition a 3D-informed diffusion model, enabling high-fidelity and temporally consistent novel view generation along arbitrary camera trajectories. 
To further enhance scalability and generalization, we develop the Event-DL3DV dataset, a large-scale 3D benchmark pairing diverse synthetic event streams with photorealistic multi-view RGB images and depth maps. 
Extensive experiments on both real-world and synthetic event data demonstrate that EA3D consistently outperforms optimization-based and generalizable baselines, achieving superior fidelity and cross-scene generalization.

Published as a conference paper at ICLR 2026
EA3D: EVENT-AUGMENTED 3D DIFFUSION FOR GEN- ERALIZABLE NOVEL VIEW SYNTHESIS
Wangbo Yu Chaoran Feng"* * Jianing Lis Aofan Zhang' Zhenyu Tang
Mingyi Guo! Wei Zhang Zhengyu Ma²
Li Yuan!.21 Yonghong Tian
SShenzhen Graduate School, Peking University
"Peng Cheng Laboratory
School of Computer and Science, Peking University
'Dalian University of Technology
ABSTRACT
We introduce EA3D, Event-Augmented 3D Diffusion framework for general- izable novel view synthesis from event streams and sparse RGB inputs. Existing approaches either rely solely on RGB frames for generalizable synthesis, which limits their robustness under rapid camera motion, or require per-scene optimization to exploit event data, undermining scalability. EA3D addresses these limitations by jointly leveraging the complementary strengths of asynchronous events and RGB imagery. its core lies learnable FA-Renderer, which constructs view-dependent 3D features within target camera frustums by fusing appearance cues from RGB frames with geometric structure extracted from adaptively sliced event voxels. These features condition 3D-informed diffussoon model, enabling high-fidelity and temporally consistent novel view generation along arbitrary camera trajectories. To further enhance scalability and generalization, dataset, a large-scale 3D benchmark pairing diverse synthetic event streams with photorealistic multi-view RGB images and depth maps. Extensive experiments on both world and synthetic event data demonstrate that EA3D consistently outperforms optimization- based and generalizable baselines, achieving superior fidelity and cross-scene generalization.
develop the DveN
INTRODUCTION
Novel view synthesis and 3D scene reconstruction are fundamental tasks in computer vision, with broad applications in robotics (Rosinol et al., 2023; Zhu et al., 2022; Yen-Chen et al., 2021), au- tonomous driving (Yan al., 2024; Lindström al., 2024; Chen al., 2025b), scene understand- ing (Kerr et al., 2023; Liu et al., 2023a; 2024b), and beyond. Recent advances in Neural Radiance Fields (NeRFs) (Mildenhall et al., 2020) and 3D Gaussian Splatting (3DGS) (Kerbl et al., 2023) have substantially improved the photorealism of novel-view rendering by learning dense and continuous scene representations. Despite their success, these approaches often struggle under high-speed motion and fail to generalize new scenarios, primarily due to their dependence on densely sampled RGB frames and the need for per-scene optimization.
In novel view synthesis under fast camera motion, two major challenges undermine the performance of traditional NeRF- and 3DGS-based methods. First, rapid motion often limits the number of available training views, leading training views or convergence to trivial solutions. Second, large inter-frame distance caused by fast camera motion violates the smooth motion assumptions underlying feature matching, often resulting in unreliable initialization of camera poses in SfM pipelines (Schonberger & Frahm, 2016), which subsequently affects the optimization of NeRF (Mildenhall et al., 2020) or 3DGS (Kerbl et al., 2023). To address these challenges, recent works (Klenk et al., 2023; Rudnev et al., 2023; Xiong al., 2024; Han et al., 2024; Cannici & Scaramuzza, 2024a; Qi et al., 2023; Yu al., 2024a; Feng et al., 2025; Low & Lee, 2023; Cannici & Scaramuzza, 2024b; Bhattacharya et al., 2024; Hwang
under-constrained reconstruction problem and causing overfitting to
"Equal contribution.
Joint corresponding authors.

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Published as a conference paper at ICLR 2026
et al., 2023; Zahid et al., 2025; Yin et al., 2024; Liao et al., 2024) have explored the use of event cameras for novel view synthesis. Event streams captured by event cameras provide temporally dense, low-latency geometric cues that remain robust under fast motion and challenging lighting conditions, making them highly complementary to conventional frame-based cameras. In particular, several methods (Liao al., 2024; Klenk et al., 2023; Xiong al., 2024) demonstrate that fusing sparse RGB frames with continuous event streams enables accurate 3D reconstruction and novel view synthesis under fast camera motion. However, these approaches still rely on optimization-basel 3D representations (Mildenhall et al., 2020; Kerbl et al., 2023), limiting their generalization to unseen environments. On the other hand, generalizable novel view synthesis methods (Yu al., 2021; Xu et al., 2024b; Rockwell et al., 2021; Chen et al., 2024b; Liu et al., 2024a; Yu et al., 2025e; Jin et al., 2024) learn strong priors over structure and appearance from large-scale multi-view datasets (Ling et al., 2024; Zhou et al., 201 8; Reizenstein et al., 2022002b Yu et al., 2023b). However, their performance often deteriorates in the presence of wide-baseline input views.
To address these challenges, we propose EA3D, Event-Augmented 3D Diffussin model for generalizable novel view synthesis from sparse RGB frames and continuous event streams. Our model consists of two main components: Firstly, drawing inspiration from cost volume-base novel view synthesis methods (Xu et al., 2024b; Chen et al., 2021; Liu et al., 2024c), we learn an Event- Augmented Feature Renderer (EA-Renderer) generate 3D features for each target view along the novel trajectory, projecting both the appearance information from the RGB frames and the occlusion- resilient geometry information from the unposed event streams into target camera frustums. Secondly, we train 3D-informed diffusion model conditioned on the 3D features, iteratively decoding these 3D features into consistent and photorealistic novel views. To support large-scale training and encourage strong generalization ability of our model, we introduce the Event-DL33D dataset, large-scale multi-view dataset consisting of real-world novel view sequences (Ling et al., 2024), diverse simulated event streams with randomized contrast thresholds, and per-view depth maps. Our model trained end-to-end on the curated dataset and generalizes well to real-world event streams without requiring per-scene optimization.
Our main contributions are summarized as follows:
We propose FA3D, the first generalizable framework for high-fidelity novel view synthesis from event streams and sparse RGB frames. To enable large-scale training and improve generalization across diverse scenes, we also introduce Event-DL3DV, large-scale 3D dataset that pairs synthetic events with photorealistic multi-view RGB images and depth maps.
We conduct extensive evaluations on both real-world event data and in in-the-wild scenes, showing that EA3D consistently outperforms optimization-baat bastod and generalizable baselines, and demonstrates strong generalization across diverse scenarios.
2 RELATED WORKS
2.1 EVENT CAMERAS
Event cameras are bio-inspired sensors that offer high dynamic range and microsecond-level temporal resolution, making them well-suited for computer vision tasks in challenging conditions such fast motion (Gallego et al., 2020; Brandli et al., 2014; Messikommer et al., 2025; Lin et al., 2023; Weng al., 2021; Wan et al., 2025; Tulyakov al., 2021a; Bardow al., 2016; Pan al., 2020; Zhu et al., 2019; Sun et al., 2022; Xu et al., 2024c). In novel view synthesis, recent studies demonstrate the effectiveness of event streams improve performance under rapid motion (Klenk al., 2023: Rudnev et al., 2023; Xiong et al., 2024; Han et al., 2024; Qi et al., 2023; Yu et al., 2024a; Feng et al., 2025). Notably, E-NeRF (Klenk et al., 2023), EF-3DGS (Liao et al., 2024) and Event3DGS (Xiong et al., 2024) achieve promising results by leveraging event streams and RGB frames for photorealistie novel view synthesis under fast camera motion. addition, several recent works integrate event streams with 3DGS related 3D reconstruction pipelines to address real-time rendering and motion blur. EventSplat incorporates event data into 3DGS to achieve real-time rendering from fast-moving event cameras (Yura et al., 2025). E2GS uses event streams enhance 3DGS in the presence of motion blur and challenging illumination (Deguchi et al., 2024). DiET-GS combines events with diffusion priors reconstruct sharp 3DGS scenes from heavily notionn-bluree images (Lee & Lee, 2025),
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while DeblurSplat employs events in SfM-free pipeline for robust 3DGS- -bssdd deblurring (Li et al., 2025). CGS-SLAM leverages events to improve the robustness of RGB-D Gaussian Splatting SLAM under fast motion (Chen et al., 2025a), and E NeRF exploits event streams to build efficient NeRFs from blurry images (Qi et al., 2024). However, these approaches still rely on optimizing separate scene-specific representation and not designed to serve general, training-free generative prior for event-augmented novel view synthesis across diverse scenes, In parallel, event cameras have advanced video frame interpolation (VFI), especially under large motion and motion blur where traditional RGB-based methods struggle. By capturing fine- fine-grained motion cues, event streams help infer intermediate frames based on sparse frames more accurately (Tulyakov al., 202 lb; Paikin et al., 2021; He et al., 2022; Weng et al., 2023; Kim et al., 2023; Ma et al., 2024). Recently, generative approaches have further enhanced realism and generalization. EGVD (Zhang et al., 2025) introduced an event-guided video diffusion model for handling large motions, while others (Chen et al.. 2024a) repurposed pretrained diffusion models for event-based interpolation. These methods focus on interpolating frames strictly along the event camera trajectory, and lack the ability to synthesize novel views from unseen views. Compared with above methods, our method enables high-quality novel view synthesis along flexible camera trajectories, and demonstrates strong generalization across diverse scenes.
2.2 DIFFUSION MODEL-BASED NOVEL VIEW SYNTIESSS
Diffusion models (Ho et al., 2020; Song et al., 2021; Rombach et al., 2022b) have shown strong potential for novel view synthesis from sparse inputs. GeNVS (Chan et al., 2023) and Zero-I-to-3 (Liu et al., 2023b) learn pose- pose-conditioned diffusion models on large-scale datasets (Reizenstein et al., 2021a; Deitke et al., 2023; Chang et al., 2015), but are limited to specific categories (Watson et al.. 2023) or synthetic scenes. While ZeroN'S (Sargent et al., 2024) and ReconFusion (Wu et al., 2024b) improve generation diversity, they are built on image diffusion models that synthesize each frame independently without explicitly modeling inter-frame dependencies, and therefore cannot enforce temporal consistency especially under large camera motions. Other works (Zhang et al., 2024; Chung al., 2023; Shriram et al., 2024; Tang al., 2025) refine warped depth-based views using pre-trained T2I diffusion models (Rombach et al., 2022a), often introducing artifacts in the inpainted region. More recently, video diffusion models have been explored for consistent novel view synthesis (Wang et al., 2024; Xu et al., 2024a; He et al., 2024; Sun et al., 2024). Several works employ point-based representations guide novel view synthesis (You al., 2025; Yu et al., 2025c;a). Others employ con-itooco video diffusion pipelines (He et al., 2024; Xu et al., 2024a; Gao et al., 2024; Wu al., 2024a; Yu et al., 2023a; 2024b; Zhou et al., 2025; Yu et al., 2025b; Ma al., 2025). Although these models learn strong priors from large-scale multi-view datasets, their inability to leverage event data limits performance in challenging fast-motion and motion-blut scenarios.
3 METHOD
As shown in Fig. 1, EA3D consists of two key components. First, given novel view camera trajectory, an Event-AAgmenter Feature Renderer GEA-Renderer projects the continuous event streams and the sparse RGB frames into 3D features for each target camera frustum. Second, 3D-aware diffusion model takes the resulting 3D features as input and synthesizes photorealistic novel views. Below we describe the core components of our method. More details about model architecture and dataset curation are provided in Appendix A.
3.1 EVENT-AUGMMETER FRATURE RENDERER
The event stream captured during fast camera motion provides dense and temporally continuous geometric prior of the 3D scene. However, event stream inherently lacks appearance information such as color and texture. In contrast, conventional RGB frames contain rich appearance content but offer only sparse and incomplete geometric cues, especially in the presence of fast camera motion. To synthesize photorealistic novel views, we propose learnable EEA-Rnddrrer that unifies both modalities into a consistent, 3D-aware feature representation. Without loss of generality, taking novel view synthesis from two RGB frames (I,, L,, ) and the continuous event stream E(to, &1) captured in between as an example, where the general multi-vien view case can be naturally decomposed into a set of two-view subproblems. Given novel-view camera trajectory (T J= between the two frames, the
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Inputs
EA-Renderer
3D Diffusion
Event Stream
3D Features
Noise
Adaptive Slicing
Event Encoder
Patch Embedding
Diffusion Transformers
Fusion
Goometry Features
RGB
Appearance
Camera
Feature Extraction
Frustums
RGB Frames
Novel Views
Figure I: Overview EA3D. Given a set of sparse RGB frames and continuous event streams, we learn an Event-Auge Evvent-uugmeteed Feature Renderer (EA- (EA-Renderer) construct new-dependent 3D features by projecting both appearance cues from RGB frames and occlusion-resilien geometry features from adaptively sliced event voxel grids into each target camera frustum. These 3D features are then passed into a conditional video diffusion model as 3D conditions, facilitating photorealistic and consistent novel view synthesis.
EA-Renderer renders along a camera trajectory and produces a sequence of 3D features Fan aligned with the target camera frustums. The EA-Renderer is structured in three stages: Appearance Feature Extraction, Event Feature Extraction and Feature Fusion.
Appearance Feature Extraction Given a pair of RGB frames (I,, I,, we first obtain their camera parameters and depths using aff-the-shelf multi-view stereo model (Wang al., 2025). These RGB frames are then projected into each camera frustum of the novel view trajectory {T (T) producing a sequence of view projections {Pi) encoder Euppr, resulting view-wise appearance feature maps:
Ji=1, which are then passed through an appearance
F
N appe ri=1 i=1 Eaper ( 1
(I)
These appearance features contain rich texture information. However, due to large view baselines and occlusions between the RGB frames, they fail capture the complete scene geometry. Therefore, further introduce geometry cues derived from the event streams.
Event Feature Extraction The continuous event stream offers microsecond-lewe latency and an extremely high temporal resolution, which provides temporally dense, Declusion-resilien observations of the 3D scene. extract geometric information from the event stream, we adopt a voxel grid-based event representation (Gallego et al., 2020) and leverage adaptive slicing strategy to obtain event voxel grids. Specifically, we first partition the continuous event stream E(4p, 41) into temporal segments. For each segment, construct two temporally overlapping slices: short slice containing m events to preserve short-term scene information, and long slice containing 2m events to capture longer temporal context. To ensure sufficient voxel density under the non-uniform event stream, the time duration of each slice is adaptively adjusted until the required number of events is accumulated. The resulting short slices and N long slices are then combined along the channel dimension, yielding a temporally enriched event voxel grid {E' N project the event voxel grid into event features:
We then learn an event encoder Eevent to Ji=1
F Pevent Eevent
(EAN
The resulting event features F Fevent encode structural continuity and occlusion-resilient geometry. However, since obtaining accurate poses and depths for event streams is non-trivial, directly projecting them into the target camera frustum along the novel view trajectory is challenging. In addition, event features inherently lack appearance cues such as color and texture. To address these issues, we introduce feature fusion module to integrate the event features with appearance features.
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Feature Fusion We learn cross-attention layer to fuse the un un-posed event features with the posed appearance features. For each appearance feature appr in the novel view trajectory, we map appe into query matrix and map the entire event features F Fevent into key and value matrices, then computing attention map to obtain the final 3D features:
F / = [Attention(((Fi)g) appr), (Feven)) ( event))
The resulting (D | project occlusion-resilinnt geometry priors and appearance information into each target camera frustums of the novel view trajectory, serving as strong 3D prior to guide the diffusion model to generate high-quality novel views.
3.2 3D-AWARE DIFFUSION MODEL.
As shown in Fig. I, given novel-view camera trajectory [T Ji=l, we first render the 3D features F3D using our EA-Renderer. We then learn a conditional distribution I ~ p[I F3D) to map these features into high-quality novel-view {T') Ji- To encourage 3D consistency in novel view synthesis, we model the conditional distribution using a video diffusion model conditioned on Fnn- Our implementation builds upon the open-sourced image-to-video generation variant of Cog VideoX (Yang et al., 2024), which adopts Diffusion Transformers with 3D self-attention for spatio-temporally coherent image-to- video generation, making it highly suitable for our setting.
Originally, VideoX is designed to take a single input image of shape H x W x 3 and generate video of shape N H x W 3. To match the temporal length of the target video, the input image is temporally padded to construct a condition video of shape N x H x W x 3. which is then passed through the 3D VAE encoder of VogVideoX to obtain a condition feature with shape of N ! W. C. This condition feature is concatenated with sampled Gaussian noise and 8 transformed into tokens via a patch embedding layer. The tokens are then iteratively refined by Diffusion Transformer (DiT) blocks (Peebles & Xie, 2023) during the denoising process. Finally, the clean latent is unpatchified and decoded by the VAE decoder to reconstruct the output video.
T & X
To adapt Cog V (Yang et al., 2024) for the novel view synthesis task, we replace its original image-based condition feature with 3D features FnD rendered by our EA-Renderer. this adaptation, also repurpose the space-time VAE encoder from Cog VideoX our appearance encoder Sappe to reduce the domain gap and facilitate convergence during training. Since the 3D VAE encoder of Cog VideoX includes temporal compression mechanism, the number of output appearance features F appe exactly reduced to 4 Consequently, the final output 3D feature FAD has a shape of N И W.
T g X & C. is then concatenated with Gaussian noise and patchified with a newly initialized patch embedding layer to form tokens matching the original input size of the DiT blocks. The noisy tokens are subsequently denoised by the DiT blocks, and finally unpatchified and decoded by the VAE decoder to reconstruct the novel views {T]
fi=1-
3.3 TRAINING DETAILS
The model is trained end-to-end using combination of diffusion loss and reconstruction loss, both weighted equally. For the diffusion loss, we adopt the standard noise schedule and loss formulation from Cog VideoX (Yang et al., 2024), enabling compatibility with pretrained weights and stable convergence in the 3D-aware generation setting:
Laiftussion FEr,Foo.t,,, [[le (I, l, FaD)
To further stabilize training and accelerate convergence, we impose reconstruction loss between the 3D features aD rendered from EA-Renderer and the ground-truth novel view features Exppe (I) obtained from the 3D VAE encoder of Cog VideoX (Yang et al., 2024):
(5) During training, we jointly optimize the event encoder and the feature fusion module in the EA- Renderer, as well as the patch embedding layer and the DiT blocks in the video diffusion model. The training resolution is fixed at 384 x 672, with a novel view sequence length of 49 frames. The event stream is sliced by uniformly sampling m [1 x 10a, 3 x 10*), which enhances robustness to event stream fluctuations. Training is conducted for 12,000 iterations using a mini batch size across GPUs (each with 80 GB of memory), with learning rate set to 10-5
Cresan
F Eappe(1)
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Input
GT
Ours
ViewCrafier
1mpu112
DGS
E-NeRF
NVS-Solver
Input]
GT
Ours
Crawerafter
Inpur2
Event3DGS
E-NeRF
NVS-Solver
Input1
GT
Ours
ViewCrafter
Inpu12
Eivet3DDSS
E-NeRF
NVS-Solver
Figure Qualitative comparison in-the-wild scenes. We show results the challenging 2-view input setting with large view baselines. ViewCrafter (Yu et al., 2025c) and NVS-Solver (You et al., 2025) exhibit visible artifacts and geometry degradation. In contrast, our method reconstructs sharper textures and more complete geometry by leveraging temporally dense geometric priors from events.
Table 1: Quantitative comparison on in-the-wild scenes.
We evaluate our model on the DL3DV (Ling al., 2024) and Lanks-and-Templer (Knapitsch al., 2017) (T&T) benchmarks under 2, 4, and 6 input views.
Method
Views
Views
Views
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR t SSIM LPIPS 1
F-NeRF (Klenk et al., 2023)
18.01
0.627
0.314
22.97
0.720
0.233
25.19
0.778
0.212
DL3DV
Event3DGS (Han al., 2024)
16.84
0.505
0.431
22.10
0.715
0.263
25.26
0.800
0.189
View Craterr (Yu et al., 2025c)
19.10
0.698
0.324
20.78
0.737
0.261
22.51
0.732
0.230
NVS-Solver (You al., 2025)
17.75
0.633
0.340
21.83
0.702
0.277
22.18
0.725
0.241
Ours
22.82
0.732
0.251
24.80
0.793
0.186
25.41
0.830
0.166
E-NeRF (Klenk et al., 2023)
22.96
0.651
0.302
25. 46
0.748
0.241
26.21
0.787
0.212
Eveet&DGS (Han al., 2024)
22.42
0.632
0.319
25.54
0.754
0.237
26.32
0.791
0.206
T&T
View CietCrafter (Yu ct al., 2025c)
18.24
0.607
0.289
22.26
0.754
0.251
22.87
0.793
0.213
NVS-Solver (You et al., 2025)
17.68
0.615
0.313
20.57
0.688
0.296
20.85
0.721
0,269
Ours
23.50
0.756
0.218
24.77
0.780
0.183
25.84
0.831
0.165

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Published as a conference paper at ICLR 2026
4 EXPERIMENTS
We begin by summarizing the evaluation setup in Sec. 4.1. Sec. 4.2 then reports quantitative and qualitative comparisons on the benchmarks. Sec. 4.3 presents ablation studies validating our model design and training losses. Appendix B.2 reports additional runtime and memory analysis, while Appendix B.3-B.7 present further ablations on robustness to trajectory misalignment, motion blur, contrast thresholds, and additional perceptual comparisons.
4.1 EXPERIMENTAL SETTING
Comparison Baselines We compare our method against both optimization-basee methods and RGB-only generalizable novel view synthesis approaches. For optimization-base baselines, we include Event3DGS (Han et al., 2024), which integrates asynchronous event streams into the 3DGS optimization. It can also be adapted to incorporate RGB frames as additional supervision for colored novel view synthesis. We also compare with E-NeRF (Klenk al., 2023), which extends NeRF-based novel view synthesis to event cameras by reconstructing continuous radiance fields from temporally aggregated event streams. E-NeRF supports hybrid supervision and can generate photorealistic views by incorporating sparse RGB frames during training. For RGB-only generalizable baselines, we include NVS-Solver (You al., 2025), method that utilizes video diffusion model (Blattmann et al., 2023) to inpaint depth-warped views for novel view synthesis. We also compare with ViewCrafter (Yu al., 2025c). which integrates point-based 3D reconstruction with video diffusion model, enabling novel view generation from sparse RGB inputs with explicit camera pose control.
Evaluation Data We first evaluate our model on mn-the-wild scenes to assess its generalization ability across diverse environments. Then, we conduct experiments datasets containing real event data to verify the robustness of our model under real-world event inputs. For the in-the-wild scene comparison, we use 140 test scenes from the DL3DV benchmark (Ling et al., 2024) that do not overlap with our training data, as well as 10 scenes from the Tanks-and- Sonks-and-Temples (T&T) (Knapitsch et al., 2017) dataset, for both qualitative and quantitative evaluation. For the real event data comparison, since there are no existing novel view synthesis benchmarks that include both sharp RGB frames and event data, we filter out 7 static sequences from the DSEC (Gehrig al., 2021) dataset that contains both sharp RGB frames and real-captured event data for evaluation.
Evaluation Setting Given test novel view sequence from the evaluation dataset, we experiment with novel view synthesis under 2, 4, and 6 input views. For the optimization- based baselines E-NeRF (Klenk et al., 2023) and Event3DGS (Han et al., 2024), since they are designed to synthesize novel views along the event camera trajectory, we follow their original setting and simulate event streams directly from the ground truth novel view sequence using vid2e (Hu et al., 2021) to ensure their rendered novel views align with ground truth. contrast, our method is designed to support novel view synthesis along flexible camera trajectories without requiring strict alignment to the event camera trajectory. To verify this capability, we sample sparse frames that do not overlap with the ground truth views from the test sequence, and use vid2e simulate event streams based these sampled frames. As a result, the simulated event stream used in our method is misaligned with the ground truth novel views, posing more general and challenging setting. For fairness, all simulations are performed with the same contrast threshold range and event simulator configuration.
4.2 COMPARISON RESULTS
In-the-wild Scene Comparison We evaluate our method on DL3DV (Ling et al., 2024) and Tanks- and-Temples (T&T) (Knapitsch et al., 2017), under 2, 4, and 6 input views. Table reports the quantitative results. Compared to the optimization-basee baselines (E-NeRF (Klenk et al., 2023) and Event3DGS (Han al., 2024)), our generalizable model achieves the highest performance in the most challenging 2-view setting. For 4-view and 6-view inputs, our model achieves comparable or better results, demonstrating the strong generalization ability of our method across different scenes. Compared to generalizable RGB-only baselines (ViewCrafter (Yu et al., 2025c) and NVS- Solver (You et al., 2025)). our method consistently outperforms these methods across all settings, which confirms the effectiveness of our event-augment design in enhancing generation fidelity under large viewpoint changes. For qualitative comparison, Fig. 2 visualizes representative synthesis results. It can be found that results of ViewCrafter (Yu al., 2025c) and NVS-Solver (You et al.,
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Inputl
GT
Ours
ViewCrafter
Inpur2
Eve
E-NeRF
NVS-Solver
Impusl
GT
Ours
ViewCrffier
Imput2
Eve
E-NeRF
NVS-Solver
Figure 3: Qualitative comparison on real event data. Our method produces sharper textures and more complete geometry compared both optimization- and RGB-only baselines, demonstrat- ing its robustness under real-world event inputs.
Quantitative comparison on real event data. We report performance under 2, 4, and Table 2:
input views the DSEC dataset (Gehrig al., 2021).
2 Views
4 Views
6 Views
Method
PSNR t SSIM
LPIPS
PSNR t SSIM f LPIPS
PSNR
SSIM
LPIPS
E-NeRF (Klenk al., 2023)
0.622
0.503
22.03
0.763
0.369
23.25
0.816
0.345
EventäDGS (Han al., 2024)
14.63
0.605
0.518
21.46
0.745
0.377
22.98
0.802
0.361
ViewCrafter (Yu et al., 2025c)
18.71
0.684
0.279
21.65
0.752
0.232
22.50
0.785
0.261
NVS-Solver (You et al,, 2025)
18.68
0.689
0.283
21.49
0.736
0.247
21.61
0.777
0.252
Ours
24.89
0.792
0.211
26.31
0.827
0.195
26.87
0.835
0.177
2025) suffer from structural distortions and texture inconsistencies under large viewpoint changes. E-NeRF (Klenk et al., 2023) and Event3DGS (Han al., 2024) also suffer from artifacts introduced by the optimization process. In comparison, our method produces more complete geometry and sharper textures, demonstrating the benefits of incorporating temporally dense event information into novel view synthesis.
Real Event Data Comparison To evaluate the robustness of our method under real-world event streams, we conduct experiments on static driving scenes selected from the DSEC dataset (Gehrig et al., 2021), which provides synchronized event data and sharp RGB frames. Quantitative results under 2, 4, and 6 input views are summarized Table Our method achieves the best performance across all metrics and view settings. Qualitative results are visualized in Fig. 3, where our method generates more accurate spatial layouts compared the baselines. These results highlight our model's ability to generalize from simulated to real event data.
4.3 ABLATION STUDY
To ablate model design choices and training losses, conduct series of experiments the Tanks-and- aemells (Knapitsch et al., 2017) dataset and real event data from the DSEC (Gehrig et al., 2021) dataset under the challenging 2-view input setting.
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Table 3:
Quantitative ablation on model design and training loss. Experiments are conducted under the challenging 2-view setting on the lanks-and-Templl (Knapitsch et al., 2017) benchmark and real event data from the DSEC (Gehrig et al., 2021) dataset.
Model Variant
T&T
Real Event Data
PSNR
SSIM
LPIPS 4
PSNR
SSIM
LPIPS 4
Geometry Feature
18.87
0.631
0.285
18.90
0.672
0.275
w/o Reconstruction Loss
20.39
0.670
0.271
19.82
0.651
0,280
w/o Adaptive Slicing
22.96
0.724
0.235
23.06
0.778
0.248
Ours
23.50
0.756
0.218
24.85
0.789
0.215
GT
w/o Geometry Feature
w/o Recon. Loss
w/o Adaptive Slicing
Ours
Figure 4: Qualitative ablation on the model design and training loss. Experiment conducted under the challenging 2-view setting.
Effectiveness of Geometry Features from Event Streams Firstly, to verify the importance of geometry features extracted from event streams, we train an ablated variant of our model by removing the event encoder and the feature fusion module, feeding only the appearance features into the diffusion model. As shown in Table and Fig. 4, the performance drops significantly in the challenging 2-view setting, where the appearance features alone provide insufficient geometric cues to resolve occlusions and maintain structural consistency.
In addition, to validate the effective- ness of geometry features under large- baseline conditions where appearance features alone provide little to vis- ible overlap, we compare the perfor- mance change between full model and the ablated variant without ge- ometry features. Specifically, we de- fine the view range as the number of frames between the two input views selected from the test sequence. To simulate increasingly challenging conditions, we progressively enlarge this inter- distance by increasing the number of intermediate frames between the two inputs. Starting from the basic view range adopted Table 3, we expand the test view range to 1.5x, 2x, 2.5x, and 3x of the base view range, thereby creating scenarios with decreasing appearance overlap and greater geometric ambiguity. The results are visualized in Fig. 5, showing that by incorporating geometry features extracted from event streams, our model maintains more stable performance as the view range increases.
PSWR
SSIM
LPPS
0.32
0.77
2.45
2.4
0.500
0.55
0.52
Cove Paals
2.4%
8.2h
1.30
View Range
View Range
View Range
Figure 5: Ablation on the effectiveness geometry features extracted from event streams under increasing view range.
Effectiveness of Adaptive Event Slicing We evaluate the effectiveness of the adaptive slicing strategy in event feature extraction by comparing it with naive fixed duration slicing baseline. As shown in Table 3 and Fig. 4, incorporating adaptive slicing improves novel view synthesis quality and reduces artifacts in the generated results.
To assess the impact of the reconstruction loss Liccun conduct an ablation study by training our model using only the diffusion loss Laimtssion As shown in Table 3 and Fig. 4, removing Lreom leads noticeable degradation in both structural consistency and
Ablation Reconstruction Loss

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Published as a conference paper at ICLR 2026
perceptual quality. This demonstrates that explicitly supervising the EA-Renderer helps align the feature space and improve generation fidelity.
5 CONCLUSION
We presented EA3D, generalizable framework that unifies event streams and sparse RGB frames for high-fidelity novel view synthesis. By introducing learnable Event- -vent-Augmented Feature Renderer (EA-Renderer) and conditioning a 31D-informmd diffusion model on fused 3D features, our method effectively captures both geometric continuity from events and appearance richness from RGB inputs. To support large-scale learning, we constructed the Event-DL3DV dataset, which provides RGB images paired with simulated event streams and dense depth maps. Extensive experiments on both synthetic and real-world benchmarks demonstrate that EA3D outperforms existing aptimization-base and generalizable methods, especially in challenging scenarios with large viewpoint baselines.
Limitations As a diffusion-basee framework, method still faces limitations in inference effi- ciency. In addition, the model may encounter challenges when the input views are of extremely low quality, making the initial MVS step produce inaccurate camera poses making model difficult to extract appearance information across views.
REPRODUCIBILITY STATEMENT
Implementation details for EA3D are provided in Sec. 3 and Appendix A. Upon publication, we will release the complete codebase and processed datasets facilitate full reproducibility.
ACKNOWLEDGMENTS
This work supported in part by the Natural Science Foundation of China (No. 62332002, 12425101),The Guangdong Grants (Grant No.20232T10X075), and Shenzhen Science and Technol- ogy PTogam(KKKTDD0224079900000011
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This supplementary material provides expanded implementation details, additional experiments, and broader impact discussion. Sections A, B, and C outline the relevant additions referenced in the main text.
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A IMPLEMENTATION DETAILS
A.1 MODEL ARCHITECTURE
Event Stream Processing We adopt voxel grid-based event representation (Gallego al., 2020) and leverage an adaptive slicing strategy obtain event voxel grids. As shown in Fig. 6, the continuous event stream E(to, t1) is first par- titioned into N temporal segments. For each seg- ment, we construct two temporally overlapping slices: short slice containing m events pre- serve short-term scene information, and long slice containing 2m events to capture longer temporal context. To ensure sufficient voxel density under the non-uniform event stream, the time duration of each slice is adaptively adjusted until the required number of events is accumulatt ..cdd The resulting N short slices and N long slices are then combined along the channel dimension to form a two-channel voxel input (Ei) Ji=1 where E, "xxx22
Temporal Segments
Short Slice
Long Slice
Figure 6: Illustration of adative event slicing.
The encoder consists of four 3D convolutional blocks with increasing channels downsampling via kernel size 3 x 3 x 3 and stride 2 x 2 x 2. followed by group normalization and ReLU activation of each block. The output geometry feature is a compact representation Fevent with shape &
Event Encoder
and
8 N H W C. These features encode occlusion-resilient structure and are fused with appearance features through cross-attention for view-conditioned 3D feature generation. We provide a visualization of the learned event features in Fig. 8. It can be observed that the event features effectively capture scene structure.
Feature Fusion We adopt Perceiver cross-attention (Jaegle et al., 2021) to fuse the posed appearance features with the unposed event features for view-informed 3D feature generation. For each novel- view appearance feature F appr IR # _ xC first flatten into sequence of N₁ tokens and apply a linear layer to project it into query matrix Q. The shared event feature volume Fevent E R x XxC is similarly reshaped into N₂ = H W tokens and linearly projected to K, V matrices.
w
then apply perceiver pross-attention to obtain the final output 3D feature, and reshape it back to the original spatial size to obtain Fjp- E R # xc, Finally, we concatenate the per-frame fused features along the temporal axis obtain the full 3D feature FıD E IR # 4 xc which serves as the input to the diffusion model for temporally coherent novel view synthesis.
A.2 DATASET
We introduce Event-DL.3DV, which augments the DL.DVV dataset (Ling et al., 2024) with event streams and depths. Specifically, the original DL.3DV dataset contains 10,000 diverse static 3D scenes with multi-view RGB images. For each view sequence in DL3DV, we simulate event streams using the event simulator vid2e (Hu al., 2021). We use the multi-view stereo model VGGT (Wang et al., 2025) to compute per-frame depths and generate RGB projections along the estimated camera trajectory, which are used for appearance feature extraction. In total, we generate 10,000 sequences with event streams, ground-truth novel views and depths.
Event Threshold Augmentation During event stream simulation, the event triggering is based on change log intensity at each pixel exceeding a contrast threshold, mimicking the behavior of real event cameras. Following the stochastic simulation strategy proposed in E2VID (Rebecq et al., 2019), we introduce randomness into the threshold selection by sampling positive and negative thresholds from uniform distribution (0.005, 0.3). This enables the generation of event streams with varying sensitivity levels and sparsity, enhancing the diversity of the training data.
Resolution Augmentation To simulate different types of event cameras with varying spatial resolutions, apply resolution-based augmentation the input RGB frames before event generation.
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Published as a conference paper at ICLR 2026
RGB frame
Threshold 0.05
Threshold 0.12
Threshold 0.3
11022
(240 1N0))
Figure Event simulation under different contrast thresholds and resolutions. Each row corresponds to a simulated resolution: 1024 x 576, 346 x 260, and 240 x 180, respectively. Each column shows the simulated events under different contrast thresholds: 0.05, 0.12, and 0.3. Lower thresholds lead to denser event firing with more fine-grained structure, while higher thresholds produce sparser events primarily along strong edges. To improve robustness across varying event data quality and settings, we train our model with mixed simulated events from diverse thresholds and resolutions.
Views
Fvent
Feature
Figure 8: Visualization of event features. The top row shows the RGB images of the scene, while the bottom shows the visualizations of the event features. The feature maps clearly reveal structural information such as object edges and contours, indicating that the event encoder successfully captures the geometric details the scene.
Specifically, we resize the frames to different target resolutions prior to feeding them into the event simulator. This allows devices such as the DAVIS346 (346 x 260) and DAVIS240 (240 x 180). In addition, we simulate high-resolution event streams up to 1024 x 576. While event cameras with such high resolutions not yet common, recent sensors such as the Sony IMX636 support resolutions up to 1280 x 720. We include such high-resolution streams increase training data diversity and improve the robustness of the model. Examples are shown in Fig. 7.
generate event streams that approximate the characteristics of real
Blur Augmentation Since the simulation process of vid2e (Hu et al., 2021) involves temporally upsampling RGB frames to generate dense intermediate frames for event stream generation, naturally enables the simulation of different levels of motion blur by integrating the dense intermediate
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Published as a conference paper at ICLR 2026
RGB projection
Ground truth novel view
Event stream (aligned)
Event stream (unaligned)
Figure 9: Examples of camera trajectory augmentation. To encourage the model to synthesize novel views beyond the constraints of the event camera trajectory, we introduce controlled temporal augmentations to the event stream, including forward/backward time shifts and temporal reversal, which result in unaligned event stream training. While RGB projections provide appearance and coarse viewpoint priors, they suffer from severe occlusions and distortions. In contrast, the event stream, despite being unaligned and appearance-free offers temporally dense and geometry- signals. By leveraging both modalities, our model learns generate high-quality novel views in an event camera trajectory-agnose manner.
Figure 10: Examples of evaluation camera trajectories. We visualize input cameras in red and test cameras in blue; the test cameras are not constrained to the visible regions the input views and include substantial unseen areas.
frames (Wang al., 2023; Zhao et al., 2024). We augment dataset with both sharp and motion- blurred RGB frames, allowing our model to robustly handle motion-blun bluured inputs.
Camera Trajectory Augmentation To encourage our model to synthesize novel views beyond the constraints of the event camera trajectory, we augment the event stream by introducing controlled temporal shifts. Specifically, we extend the event stream forward or backward in time relative the ground truth novel views, apply temporal reversal, resulting in deliberate misalignment between the event data and the ground truth novel views. This augmentation forces the model to rely on trajectory-agnostic structural cues from the event stream. Examples of the event stream, RGB projections, and ground-truth novel views are shown in Fig. 9. It can be observed that the RGB projections provide appearance and camera viewpoint information in the target views, but suffer from substantial occlusions and spatial distortions. In contrast, the event stream, despite being unaligned with the ground-trun -truth novel views and lacking color information, offers clear and temporally dense geometric cues of the scene structure. By leveraging both modalities to train the model, our model is able to synthesize high-quality novel views that preserve both appearance fidelity and structural consistency.
B
MORE EXPERIMENTS
B.1 DETAILED EVALUATION SETTINGS
For the mn-the-wild scene comparison, we use 140 test scenes from the DL.DD benchmark (Ling et al., 2024) that do not overlap with our training data, as well as 10 scenes from the Tanks-and- (T&T) (Knapitsch et al., 2017) dataset, with an event simulation contrast threshold of 0.2. For the real event data comparison, since there both sharp RGB frames and event data, we filter out 7 static sequences from the DSEC (Gehrig et al., 2021) driving scene dataset that contains both sharp RGB frames and real-captured event data for evaluation. Given a test novel view sequence from the evaluation dataset, we conduct experiments under 2, and 6 input view settings. We define the view range the number of frames between two
existing novel view synthesis benchmarks that include
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Published as a conference paper at ICLR 2026
Table 4: Computation cost comparison with the basclines.
Time (h)
Memory (GB)
Method
Upstream MVS
Infer.
Opt.
Total
Infer.
Opt.
E-NeRF (Klenk et al., 2023)
COLMAP (300+ s)
0.12
3.50
3.62
20
26
Event3DGS (Han al., 2024)
COLMAP (300+ s)
0.0002
0.80
0.8002
3
12
NVS-Solver (You et al., 2025)
COLMAP DA (300+ s)
0.18
0
0.18
21
0
ViewCrafter (Yu et al., 2025c)
DUSt3R (5 s)
0.06
0
0.06
24
0
Ours
VGGT (1
0.03
0
0.03
28
0
Table Ablation on robustness our method to motion-blurred input.
Method
PSNR f
SSIM f
LPIPS 4
NVS-Solver (You et al., 2025)
16.25
0.539
0.407
View ViewCrafter (Yu et al., 2025c)
16.96
0,580
0.391
Ours w/o event feature
17.62
0.629
0.355
Ours
21.54
0.713
0.230
input views selected from the test sequence. Specifically, the view range for the 2-view setting is 400 frames on DL3DV, 300 frames on Tanks-and -e---nn---eeeee and 50 frames on DSEC. As shown in Fig. 10, the test views are not limited visible regions between the input views but also cover substantial unseen regions.
B.2 OPTIMIZATION AND INFERENCE TIME COMPARISON
We report the per-scene optimization time, inference time, and GPU memory usage of all baselines on a single NVIDIA A100 GPU (40 GB) in Table 4. Although our method employs a diffusion model during inference, it still achieves substantially lower overall runtime compared to optimization- baselines, owing to its optimization-- free design. Moreover, our inference pipeline generates 49 frames in single forward pass, while each evaluation sequence contains fewer target views than the number of frames produced. As a result, the runtime remains identical across the 2-, 4-, and 6-view input settings.
B.3 ROBUSTNESS TO OUT-OF-TRAJECTOR EVENT STREAM
In this experiment, investigate the robustness of EA3D against varying degrees of misalignment between the novel view synthesis trajectory and the event camera trajectory. This analysis is essential for assessing the general applicability of our method to unconstrained real-world scenarios, where the precise camera poses of event data are often unavailable and do not align with the target novel view trajectory. To this end, we perform controlled ablation by introducing deviations between the novel view trajectory and the event camera trajectory.
Specifically, construct series of synthetic event sequences where the camera poses perturbed away from the novel view trajectory by varying amounts. The degree of mismatch is quantified using the Absolute Trajectory Error (ATE), computed as the average Euclidean distance between the event camera poses and the corresponding novel view poses. We normalize the ATE to range from [0. 1] for clarity. We evaluate EA3D's novel view synthesis performance under increasing levels of ATE between event camera trajectory and novel view trajectory, while keeping the RGB inputs and target views fixed. As shown in Fig. 11, thanks to our training strategy, the PSNR remains significantly misaligned. These results demonstrate that our EA3D does not rely on precise correspondence between the event camera trajectory and the novel view trajectory. Instead, the model effectively distills crujectory-agosste geometric cues from the event stream, enabling robust synthesis across diverse and unaligned camera trajectory.
relatively stable even when the event and novel view trajectories
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Published as a conference paper at ICLR 2026
14.30
24.30
LA30
1430
24.25
24.25
24.00
24.00
23.30
25.30
23.25
23.25
23.00
23.50
22.7
22.75
22.50
22.50
Figure 11: Ablation on robustness to misalign- ment between novel view trajectory and event camera trajectory.
Figure 12: Ablation on robustness to different Contrast Threshold.
Table 6: Ablation on robustness of our method to fast motion.
Method
PSNR f
SSIM 1
LPIPS +
NVS-Solver (You et al., 2025)
16.56
0.547
0.381
ViewCrafie (Yu al., 2025c)
16.81
0.573
0.384
Ours w/o event feature
17.10
0.615
0.367
Ours
22.05
0.725
0.221
B.4 ROBUSTNESS TO MOTION BL.R
Our Event-DL3DY dataset is simulated with the vid2e event generator (Hu et al., 2021), which temporally upsamples the RGB frames before integrating them to synthesize event streams. The intermediate frame accumulation naturally produces realistic motion blur, and we therefore expose EA3D both sharp and blurred RGB imputs during training so that the model remains reliable when motion blur is present at test time. To quantify the benefit of event guidance under motion blur, evaluate on the EvDeblurNeRF-DAAE event streams aligned with motion-blurred RGB images. We compare our full model with RGB-only baselines (NVS-Solver, ViewCrafter, and an EA3D variant without event fcatures) under the 2-view input setting. As shown in Table 5, incorporating events substantially improves reconstruction fidelity in motion blur scenarios.
dataset (Cannici & Scaramuzza, 2024b), which provides real
B.5 ROBUSTNESS TO FAST MOTION
To further demonstrate the advantage of using event cameras over standard 3D generative models under fast camera motion, additionally conduct comparison 10 drone-captured sequences with rapid motion from the M3ED (Chaney et al., 2023) dataset. As shown in Table 6, incorporating events substantially improves novel view synthesis quality in these fast-motion scenarios.
B.6 ROBUSTNESS TO CONTRAST THRESHOLD
To evaluate the robustness of EA3D to different Contrast Threshold, we conduct ablation study, Specifically, we vary the Contrast Threshold from 0.05 to 0.3 using event simulator (Hu et al., 2021). As shown in Fig. 12, EA3D maintains stable PSNR performance across all Contrast Threshold settings, with only minor fluctuations as the threshold varies. We attribute this robustness to our training strategy, which incorporates diverse set of simulated event streams generated with thresholds sampled from a wide range, thereby enabling the model to generalize across diverse event streams.
B.7 GENERATIVE ABILITY EVALUATION
Since EA3D leverages event observations that are inaccessible to purely RGB-based methods, we further evaluate perceptual quality metrics that do require aligned ground-truth, using FID on the
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Published as a conference paper at ICLR 2026
100
NVS-Solver
ViewCrafter
EA3D
90
80
70
53.9
FID
40
42.8
33.8
39.4
31.3
37.5
30.2
30
2
6
Input Views
Figure 13: FID comparison (lower is better) on Banks-and-Temple under varying input views.
Table 7: Comparison with event-based frame interpolation methods.
Method
PSNR 1
SSIM f
LPIPS +
VDM-EVFI (Chen et al., 2024a)
18.36 22.05
0.665
0.350
Ours
0.725
0.221
Lanks-and-Templer dataset. As shown in Fig. 13, compare EA3D against RGB-based generative NVS methods, NVS-Solver (You et al., 2025) and ViewCrafter (Yu et al., 2025c), under 2-, 4-, and 6-view input settings. Across all settings, EA3D achieves consistently lower FID scores than both baselines, highlighting its superior generative capability.
B.8 COMPARISON WITH EVENT-BASED FRAME INTERPOLATION METHODS
To further validate the effectiveness of our method in leveraging event data for novel view synthesis under fast camera motion, compare EA3D with the event-based frame interpolation method VDM- EVFI (Chen et al., 2024a) on 10 drone-captured sequences with fast motion from the M3ED (Chaney al., 2023) dataset. As shown in Table 7, EA3D consistently achieves higher novel-view synthesis quality than VDM-EVFI.
C BROADER IMPACT
Event-augmented novel view synthesis has the potential to advance wide range of downstream applications that require reliable 3D reconstruction in high-speed camera motion from sparse captured data. The asynchronous and low-latency nature of event streams makes them particularly well-suited for scenarios involving rapid motion limited imaging bandwidth. As such, EA3D) has the potential to benefit fields such as 3D mapping in autonomous acrial or ground robots, emergency response in visually degraded conditions, and minimally invasive medical imaging where traditional camera systems may face physical or temporal constraints. In addition, the ability to generate coherent 3D scenes from few input views aligns with the growing demand for lightweight sensing in edge devices and wearable systems. It can also support content creation and virtual environment modeling AR/VR settings, reducing the reliance on dense multi-camera rigs. We encourage responsible use of this technology and recommend that its deployment follow ethical and legal guidelines, especially in wwweelllnnc-adjacceee or sensitive applications. To support further research, we will release both codebase and the dataset to the community. In addition, developing robust watermarking mechanisms to ensure the traceability and integrity of novel views synthesized by model.
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Reviews:
[{"review_id": "vKDzEFkzDz", "reviewer": "Reviewer_gMy4", "summary": "This paper proposes a framework for generalizable novel view synthesis that uses a set of sparse RGB frames and a continuous event stream as input  to generate novel RGB views. The method conditions a video generative model (a Diffusion Transformer based on CogVideoX)  on 3D features. These 3D features are constructed by an \"EA-Renderer,\" which uses an off-the-shelf 3D reconstruction method (VGGT) to get posed appearance features, and then enriches these features with geometric information from event-based voxels. Since poses for the event camera are not available , the method learns a feature fusion module (using cross-attention)  to integrate the un-posed event features into the posed 3D features. The paper provides comparisons on real-world and synthetic datasets against both RGB-only 3D generative models and optimization-based, event-camera-specific methods. The ablation study provides a much-needed demonstration of performance degradation when the event-based stream is removed.\n\nDespite the technical contribution being somewhat incremental (combining existing MVS, video diffusion, and attention mechanisms), the application to event-augmented novel view synthesis is valuable and I am arguing for acceptance. However, I would strongly encourage the authors to address the weaknesses below -- the paper would be far more convincing if the authors added experiments on challenging scenes with real fast motion (e.g. fast-moving objects or drone footage) to truly demonstrate the advantage of using event cameras over standard generative 3D models.", "questions": "- The feature fusion module performs cross-attention between each appearance feature and the entire event feature set. How does the computational cost of this fusion scale, and does it limit the practical length of the event streams you can process?\n- How robust is the method to failures in the initial MVS (VGGT) step, especially in texture-less regions or under extreme motion?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Important, interesting, and timely problem: The paper addresses the challenging problem of novel view synthesis from sparse inputs under fast camera motion -- a scenario where event cameras offer a clear theoretical advantage over standard RGB-only methods and where existing 3D generative model research hasn't focused on .\n- Fusion Strategy, though obvious choice and computationally intensive, is an interesting technical contribution. The proposed architecture for fusing posed RGB features with un-posed event features via a cross-attention mechanism  is a logical way to combine these complementary data sources. \n- Effective Ablations: The ablation studies in Section 4.3 clearly validate the core contribution, and in particular, the main ablation shows that incorporating event-based geometry features significantly improves results, especially as the baseline (view range) between input frames increases.", "weaknesses": "- Dataset Limitations: My main concern is that the evaluation datasets do not fully demonstrate the scenarios where event cameras are organically needed. The paper uses \"static sequences\" from the real-world DSEC dataset , while other benchmarks (DL3DV, T&T) rely on simulated events. Most motion blur is artificially induced. The paper would be significantly stronger if it outperformed existing 3D generative models in datasets where event cameras are truly necessary (e.g. fast drone videos, rapid human motion).\n- Missing Comparisons: The paper does not compare against EGVD (Zhang et al., 2025) or (Chen et al., 2024a). While the authors argue these methods are for interpolation along the trajectory, the evaluations in this paper also appear to synthesize views near the original camera path. A comparison seems feasible and would benefit to fully evaluate the approach.\n- Minor: The method is heavily reliant on an off-the-shelf pretrained multi-view 3D reconstruction method (VGGT)  to extract the initial 3D point cloud and posed features.\n- Minor: Inaccuracies in Related Work (Sec 2.2): The related work section needs revision for accuracy. 1) The claim that ReconFusion / ZeroNVS \"struggles with pose control... due to implicit pose encoding\"  is incorrect as there are explicit 3D method. 2) The use of \"3D-aware diffusion model\"  is confusing. This term typically refers to models that are 3D-consistent by design (e.g. by incorporating a renderer in the denoising loop, like EG3D DMV3D and RenderDiffusion, which haven't been cited), which is not the case here. Therefore, the claim \"To ensure 3D consistency\"  should be weakened to \"To encourage 3D consistency,\" as a video diffusion model does not guarantee this. 3) The claim that other video diffusion models \"degrade when the input views exhibit large viewpoint variations\" is a weak argument; methods like CAT3D perform well from very sparse views. The primary argument against them should be their inability to leverage event data.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TsZeYFggFy", "reviewer": "Reviewer_eG5r", "summary": "The paper proposes a generalizable novel view synthesis framework that fuses event streams and RGB images to achieve photorealistic rendering under fast camera motion. The method extracts appearance features from RGB frames using a multi-view stereo model, encodes geometry-aware event features through voxel-based event representation, and integrates them via attention module. Experimental results demonstrate that the proposed fusion module effectively achieves its intended goal and enables the framework to reach state-of-the-art performance in novel view synthesis.", "questions": "1. How did you align the VGGT poses to the coordinate system used in your evaluation? I find this part a bit confusing, and I have some concerns about the fairness of the evaluation. If the authors could clarify this alignment process, I would consider increasing my score.\n\n2. Would you mind add one more ablation about without event features? Which will help us to understand why we need to do generalizable event-based novel view synthesis.\n\n3.Is your evaluation performed only between the input views, or over the entire scene including unseen regions? The results are surprisingly strong, so I’m concerned that the evaluation might be limited to the input-view range rather than the full scene.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. This paper is the first to propose a generalizable novel view synthesis framework based on event camera,. The overall network design is clear, simple, and effective, leading to strong performance.\n\n2. The use of 3D features to condition the DiT appears both effective and novel, offering a promising direction for improving 3D-aware generation.\n\n3. The final results are quite impressive in term of extremely sparse input conditions, the proposed method even outperforms per-scene optimization approaches. \n\n4. Adaptive Slicing is an effectively strategy for encoding event feature.\n\n5.The paper is well written, clearly structured, and easy to follow", "weaknesses": "1. The ablation study appears unfair to me, as the authors leverage VGGT + video diffusion, which could be the primary factor contributing to the performance improvement. From my perspective, as shown in works like FlowR [1], combining geometry foundation model + multi-view diffusion can already yield strong performance. However, the ablation study in this paper does not clearly demonstrate that the event information is fully utilized or that it significantly contributes to the performance gains.\n\n2. From Table 1, the performance improvement becomes less significant when the number of input views increases (e.g., to six views), indicating that the method’s advantage is mainly evident in extremely sparse-view settings.\n\n[1] Fischer, T., Bulò, S. R., Yang, Y. H., Keetha, N., Porzi, L., Müller, N., ... & Kontschieder, P. (2025). Flowr: Flowing from sparse to dense 3d reconstructions. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 27702-27712).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KHJoovriB9", "reviewer": "Reviewer_RKQv", "summary": "The paper proposes an event-augmented framework for novel view synthesis. The core idea is to leverage event cameras to provide complementary information to strengthen the overall synthesis process, especially in scenarios with rapid camera movements. The framework consists of two main components: an event-augmented (EA) feature renderer and a feature-conditioned video diffusion model. The EA feature renderer fuses event information with RGB features using cross-attention in the 3D space, creating a strong feature prior for subsequence generation process. The diffusion model, adapted from CogVideoX, is then conditioned on these features to synthesize novel views in video sequence. Experiments demonstrate the advantages of adopting event features to boost the performance of novel view synthesis.", "questions": "N.A.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "* The core idea of leveraging event cameras to provide complementary information for novel view synthesis is both novel and compelling.\n* The proposed framework is straightforward and effective. The experiments clearly demonstrate the benefits of incorporating event data.\n* The paper is well-written, clearly structured, and easy to understand", "weaknesses": "* A central claim of the paper is that event data is particularly helpful for fast camera movements. However, the experimental section seems to lack a direct evaluation to support this specific claim. It would significantly strengthen the paper to include an analysis comparing performance under fast camera motion.\n* As event data has strong correlation with camera trajectories, I am curious about the model's generalization capabilities under different camera trajectories. How does the method perform when tested with camera trajectories that are different from those seen during training (e.g., different paths or motion patterns)? A discussion on this aspect would be very insightful.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nPWwREgo6W", "reviewer": "Reviewer_BEQ9", "summary": "The paper introduces EA3D, a framework for generalizable novel view synthesis that uses sparse RGB frames and continuous event streams as input. It proposes a Event-Augmented Feature Renderer module, which is a learnable module that constructs 3D features for the target camera path. It extracts appearance cues from the sparse RGB frames and captures geometric structure from estimated camera parameters and depths. The 3D features from the EA-Renderer are used to condition a video diffusion model (based on CogVideoX) for novel view synthesis.", "questions": "In Table 4, \"Computation cost comparison with the baselines,\" does the reported computation cost include the time required to estimate camera parameters and depths using an off-the-shelf multi-view stereo model?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Extensive experiments: The paper provides comprehensive comparisons with baselines, investigates the impact of varying the number of input samples, and includes relevant ablation studies on model design.\n2. The creation of the large-scale Event-DL3DV dataset would be a valuable contribution if it is made open source.\n3. The proposed method achieves noticeable improvements in both visual quality and quantitative metrics.", "weaknesses": "1. Reliance on Upstream Models: The EA-Renderer requires camera parameters and depth maps for the input RGB frames, which are obtained from an \"off-the-shelf multi-view stereo model\". This means EA3D is not fully end-to-end, and its performance is dependent on the accuracy of this prerequisite model.\n2. The idea of incorporating explicit 3D information into diffusion models has been explored in many previous works, and this paper does not present particularly novel aspects in this regard.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "PhMVmKveBW", "reviewer": "Reviewer_osNE", "summary": "The paper proposes EA3D, a generalizable event-augmented 3D diffusion framework for novel view synthesis from sparse RGB frames plus continuous event streams. An EA-Renderer fuses posed RGB appearance features with geometry features extracted from adaptively sliced event voxel grids via cross-attention, producing view-conditioned 3D features that condition a CogVideoX-style 3D-aware diffusion model. The authors also curate Event-DL3DV, pairing synthetic event streams with multi-view RGB and depth. Experiments on DL3DV and Tanks-and-Temples with 2/4/6 views, plus real events from DSEC, report consistent gains over optimization-based event methods (E-NeRF, Event3DGS) and RGB-only generalizable methods (ViewCrafter, NVS-Solver). Ablations indicate benefits from event-derived geometry, adaptive slicing, and a feature reconstruction loss.", "questions": "The paper introduces several key components, such as the geometry feature extracted from event streams, the adaptive slicing strategy within the event encoder, and the 3D-aware diffusion model. However, these are all described only briefly. Could the authors provide more detailed explanations, figures, or analyses to clarify how these modules operate and how they differ from existing event-based voxel encoders or diffusion frameworks?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The authors purposed really clear and robust architecture. EA-Renderer elegantly aligns pose-free event geometry with pose-conditioned RGB appearance features in camera trajectory space, directly addressing occlusion and large-baseline challenges. The proposed Event-DL3DV uses randomized thresholds/resolutions to diversify events and adds blur augmentation and temporal reversal to enhance robustness and trajectory generalization.\n\n2.The authors systematically isolate the effects of event geometry, adaptive slicing, and reconstruction loss, showing that event features significantly stabilize NVS under extended baselines.\n\n3.They report inference and memory comparisons with E-NeRF, Event3DGS, NVS-Solver, and ViewCrafter, showing clear runtime advantages.", "weaknesses": "1.The paper includes both event-based (E-NeRF, Event3DGS) and non-event diffusion-based (ViewCrafter, NVS-Solver) baselines, which is good coverage. However, it would further strengthen the empirical section to discuss or include newer event-augmented Gaussian frameworks. Considering that many latest works have not yet released code, this omission is understandable and not a fatal limitation, but at least a discussion of such methods would provide better context for positioning EA3D among concurrent developments.\n\n2.While the proposed “geometry feature from event streams” introduces an interesting idea of leveraging voxelized event information to encode geometric continuity, the paper does not elaborate on its structure or behavior in sufficient depth. The feature appears to be embedded within the event encoder of EA-Renderer but lacks standalone analysis, visualization, or theoretical justification. Consequently, although this feature may contribute meaningfully to performance gains (as suggested by Table 3), its novelty and generalizability remain underexplored. A more detailed study would greatly strengthen this contribution.\n\n3.Because the paper does not fully characterize the geometry feature or its underlying mechanism, the overall framework risks being perceived as a system-level composition of existing modules (event voxel encoding, cross-attention fusion, and diffusion-based rendering) rather than a fundamentally new algorithmic contribution. The work would benefit from a clearer articulation of what specific modeling insight or formulation distinguishes EA3D from prior event-guided 3D reconstruction methods.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)