Sharp Monocular View Synthesis in Less Than a Second
Authors: Lars Mescheder
Wei Dong
Shiwei Li
Xuyang BAI
Marcel Santos
Peiyun Hu
Bruno Lecouat
Mingmin Zhen
Amaël Delaunoy
Tian Fang
Yanghai Tsin
Stephan Richter
Vladlen Koltun
Venue: ICLR 2026 Poster (2026)
Keywords: photorealism
view synthesis
neural rendering
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Real-time photorealistic rendering of nearby views from a single photograph

Abstract: We present SHARP, an approach to photorealistic view synthesis from a single image. Given a single photograph, SHARP regresses the parameters of a 3D Gaussian representation of the depicted scene. This is done in less than a second on a standard GPU via a single feedforward pass through a neural network. The 3D Gaussian representation produced by SHARP can then be rendered in real time, yielding high-resolution photorealistic images for nearby views. The representation is metric, with absolute scale, supporting metric camera movements. Experimental results demonstrate that SHARP delivers robust zero-shot generalization across datasets. It sets a new state of the art on multiple datasets, reducing LPIPS by 25–34% and DISTS by 21–43% versus the best prior model, while lowering the synthesis time by three orders of magnitude. Code and weights are provided at https://github.com/apple/ml-sharp.

Published as a conference paper at ICLR 2026
SHARP MONOCULAR VIEW SwwSSYN SYNTHESIS IN LESS THAN A SECOND
Lars Mescheder
Wei Dong
Shiwei Li
Xuyang Bai
Marcel Santos
Peiyun Hu
Bruno Lecouat
Mingmin Zhen
Amaël Delaunoy
Tian Fang
Yanghai Tsin
Stephan R. Richter
Vladlen Koltun
Apple
ABSTRACT
We present SHARE approach photorealistic view synthesis from sin- gle image. Given a single photograph, SHARP regresses the parameters of a 3D Gaussian representation of the depicted scene. This is done in less than sec- ond on a standard GPU via single feedforward pass through a neural network. The 3D Gaussian representation produced by SHARP can then be rendered in real time, yielding high-resolution photorealistic images for nearby views. The rep- resentation is metric, with absolute scale, supporting metric camera movements. Experimental results demonstrate that SHARP delivers robust zero-shot general- ization across datasets. sets new state of the art on multiple datasets, reducing LPIPS by 25-34% and DISTS by 21-43% versus the best prior model, while low- ering the synthesis time by three orders of magnitude.
INTRODUCTION
Imagine revisiting a precious memory cap- tured on camera. What if technology could lift the scene out the image plane, recre- ating the dicensinnnsioal world as it was then, putting you back in the scene? High-resolution low-latency AR/VR head- sets can convincingly present spatial con-
SHARP (ours)
04
Flash3D
Other methods
DISIS Score (L)
tent. 3D representations can also be ren- dered on handheld displays. Can these surfaces be used to reconnect us with our memories in new ways?
0.2
SVC
Viaterrtter
LVSM
IMPI
Ge
SHARP (ours)
Recent advances in neural render- ing (Tewari et al.) 2022) have demon- strated remarkable success in synthesizing photorealistic views, but many of the most impressive results leverage multiple input images and conduct time-consuming per- scene optimization. We are interested view synthesis from single photograph, to support real-time photorealistic rendering from nearby views. Specifically, our application setting yields the following desiderata. (a) Fast synthesis of 3D representation from single photograph, to support interactive browsing of personal photo collections. (b) Real-time photorealistic rendering
100
125
150
825
850
Latency (s)
Figure 1: Synthesis time single GPU versus image fidelity on the ScanNet++ dataset.
http://:tttbb.cco/pppll///////

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Input
SHARP (ours)
Gen3C
ViewCrafter
Plash3D
TMPI
Figure 2: SHARP synthesizes a photorealistic 3D representation from a single photograph in less than second. The synthesized representation supports high-resolution rendering of nearby views, with sharp details and fine structures, at more than 100 frames per second on standard GPU. We illustrate photographs from Unsplash (2022).
of the resulting 3D representation from nearby views. We wish support natural posture shifts in AR/VR headsets, providing the experience looking at a stable 3D scene from different perspectives, but need not support substantial travel ("walking around") within the photograph. (c) The 3D representation should be metric, with absolute scale, to accurately couple the virtual camera with physical headset another physical device.
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In this paper, present SHARP (Single-image High-Accurase Real-time Parallax), approach to meeting these desiderata. Given a photograph, SHARP produces a 3D Gaussian representa- tion (Kerbl et al. 2023 of the depicted scene via single forward pass through neural network. This representation can then be rendered in real time from nearby views. Though the high-level approach (single image in, 3D Gaussian representation out) echoes prior work, SHARP delivers state-of-the-ar visual fidelity while keeping the generation time under one second on an A100 GPU. The key ingredients are scale and a number of technical choices whose importance we validate via controlled experiments.
(See Figure I)
First, we design neural network that regresses high-resolution 3D Gaussian representation from a single photograph. While our network comprises multiple modules, it is trained end-to-end to optimize view synthesis fidelity. Second, we introduce a carefully designed loss configuration that prioritizes the accuracy of synthesized views while regularizing away common artifacts. Third, we introduce a learned depth adjustment module that is used during training facilitate view synthesis supervision in the presence of inaccurate depth estimates. Figure 2 shows some views synthesized by SHARP and a number of baselines.
We conduct a thorough experimental evaluation multiple datasets that were not used during train- ing. using powerful perceptual metrics such as LPIPS (Zhang et al. (018)) and DISTS (Ding et al. 2022) to assess image fidelity. SHARP improves image fidelity by substantial factors versus prior feedforward methods. In comparison to duffusion-based systems, SHARP delivers higher fidelity while reducing synthesis time by two three orders of magnitude. Compared to the strongest prior method (Ren et al., 2025), SHARP reduces LPIPS by 25-34% and DISTS by 21-43% across the test datasets (in the zero-shot regime), while accelerating synthesis by three orders of magnitude and producing a 3D representation that supports high-resolution rendering of nearby views at 100 frames per second.
In summary, our contributions are as follows:
End-to-end architecture: we design novel network architecture that can be trained end- to-end to predict rigo-resolution 3D Gaussian representations.
Robust and effective loss configuration: we carefully choose series of loss functions prioritize view synthesis quality while maintaining training stability and suppressing common visual artifacts.
Depth alignment module: introduce simple module that effectively resolve depth ambiguities during training, a fundamental challenge for regression-based view synthesis methods.
Using our insights, we demonstrate that state-of-the-ar high-resolution view synthesis is feasible in a purely regression-based framework.
RELATED WORK
View synthesis from multiple images. Early image-based rendering approaches synthesized new views with minimal 3D modeling. Chen & Williams (1993) introduced view interpolation, enabling transitions between captured viewpoints. QuickTime VR (Chen 1995) created navigable environ- ments from panoramic images. Layered Depth Images (Shade al. 1998) addressed occlusions by storing multiple depth values per pixel. Kang et al. (2006) survey the early years of image-based based rendering.
More recently, deep learning and GPU acceleration transformed view synthesis from multiple im- ages. Free View Synthesis (Riegler & Koltun 2020) combined geometric scaffolds with learned features to synthesize novel views from distributed viewpoints. Stable View Synthesis (Riegler & Koltun 2021) improved on this by enhancing stability and consistency across views. Neural ra- diance fields (NeRF) (Mildenhall et al.) 2020) introduced continuous implicit representations that support remarkable levels of photorealism (Barron et al. 2023). 3D Gaussian Splatting (Kerbl et al. 2023) significantly accelerated rendering while maintaining visual fidelity through explicit 3D prim- itives. We use the 3D Gaussian representation developed by Kerbl et al. (2023), but apply it in the context of view synthesis from single image.
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A number of works develop feedforward prediction models for view synthesis from small number of nearby views. IBRNet (Wang et al. 2021) generalized image- based rendering across scenes using learned features and ray transtormers. MVSNeRF (Chen al. (02I] reconstructed neural radiance
fields from a few input images via cost volume processing. LaRa (Chen et al., 2024) regressed an
object-level radiance field from sparse input images. GS-LRM (Zhang al. 2024) leveraged
transformer to predict 3D Gaussian representation from posed sparse images. Our work focuses on view synthesis from a single image.
View synthesis from a single image. The introduction of larger datasets (Zhou et al. 2018 Tung al.) 2024) enabled the transition from scene-specific multi- -iet optimization to learning based pipelines that can infer a plausible 3D representation from a single image. [Zhou et al. (2016) syn- thesized novel views from a single image through appearance flow. Subsequent work developed variants of depth-based warping (Wiles et al. 2020 Jampani et al.) (021) and multiplane images (MPI) (Zhou et al.) 2018 [Tucker & Snavely, 2020). AdaMPI (Han et 2022) adapted multiplane images to diverse scene layouts through plane depth adjustment and depth-awart color prediction, trained using a warp-back strategy on single-view image collections. Khan et al. (2023) proposed Tiled Multiplane Images (TMPI), which splits MPI into many small tiled regions with fewer depth planes per tile, reducing computational overhead while maintaining quality. Several recent methods have drawn inspiration from the success of transformers modeling long-range dependen- cies, leveraging large transformer-basee encoder-decoder architectures to infer scene structure and appearance implicitly for novel views (Hong et al. 2024 Jin al. 2025).
PixeINeRF Yu et al. (021) trained convolutional networks to predict an object-level neural radiance field from single image. Splatter Image (Szymanowicz al. 2024) introduced direct prediction of per-pixel Gaussians via a U-Net. Flash3D (Szymanowicz et al. (222aa) incorporated pre-trained
depth prediction network for generalization to more complex scenes. Schwarz et al. (2025) proposed a recipe for generating 3D worlds from single image by decomposing this task into a number of steps and leveraging diffusion models.
Diffusion models have emerged as powerful tools for novel view synthesis with sparse input, offer-
ing high-quality results through iterative denoising processes (Po et al., 2024). Watson et al. (2023)
developed an early application to view synthesis. Zero-1-to-3 (Liu et al. (2023) demonstrated zero- shot view synthesis by fine-tuning diffusion models on 3D object datasets. iNVS (Kant et al. 2023)
repurposed diffusion inpainters for view synthesis by combining monocular depth estimation with inpainting. NerfDiff (Gu et al.] (2023) distilled a 3D-aware diffusion model into NeRF by synthesiz- ing virtual views improve rendering under occlusion. These early attempts focused on object-level view synthesis.
At the scene level, ViewCrafter Yuu et al. 2025b), ZeroNVS (Sargent al. 2024), CAT3D Gao
al. 2024). SifatDiff (Zhang et al. 2025), ), Stable Virtual Camera (Zhou et al. 2025). Bolt3D Szy manowicz al.. 2025b), Wonderland (Liang et al., 2025), WonderWorld YYu al.) 2025a), See3D (Ma et al. 2025) and Gen3C (Ren el al. 2025) all applied diffusion models to view synthesis from sparse image sets or a single image. The diffusion-baii basedd approach supports impressive im- age quality from faraway viewpoints, leveraging diffusion priors to synthesize plausible appearance even for views that have no overlap with the input. On the other hand, image quality from nearby views (corresponding to natural head motion or posture shifts) can be noticeably less sharp and pho- torealistic than the input, while the synthesis time can sometimes stretch into minutes. (Although Bolt3D (Szymanowicz et al. 2025b) makes impressive progress the latter front.) In contrast, we aim for real-time rendering of maximally photorealistic high-resolution images from nearby views, supporting headbox that allows for natural posture shifts while maintaining photographic quality. Our approach generates a high-resolution 3D representation that provides such experiences from single-image input in less than a second photographs to photorealistic 3D during interactive browsing of a photo collection.
single GPU, supporting conversion of pre-existing
METHOD
3.1 OVERVIEW
Our approach, SHARP, generates a 3D Gaussian representation from a single image via a forward pass through a neural network. The input to the network is single monocular RGB image I
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Imput Image
Novel views
Layered Depth
Adjusted Depth
Base Gaussians
Image
Depth Adjustment
Gaussian Initializer
Encoder
Gaussian Renderer
Depth Decoder
Features
Gaussian
Gaussian Refinemen
Gaussian Composer
Decoder
Diffcrentiable
Oulput Caussians
Figure 3: Our model consists of four learnable modules (Section 3.1 h. a pretrained encoder for fea- ture extraction, depth decoder that produces two distinet depth layers, depth adjustment module, and a Gaussian decoder that refines all Gaussian attributes. The differentiable Gaussian initializer and composer assemble the Gaussians for the resulting 3D representation. The predicted Gaussians are rendered to the input and novel views for loss computation (Section 3.4p.
RCCH*W where C C= denotes the number of color channels, is the height, and W the width of the image. The output is set of 3D Gaussians G E R which can be rendered arbitrary views using a differentiable renderer. Here K 14 is the number of Gaussian attributes (3 for the position, for scale, for orientation, for color and for opacity) and N is the number of output Gaussians. In practice, SHARP outputs 2 x 768 x 768 z 1.2 million Gaussians per image, parameterized over a 768 x 768 grid with two layers. We do not use spherical harmonies (Kerbl er al. 2023), because the number of spherical harmonic coefficients grows quadratically with the order of the spherical harmonies and would lead to a large increase in output size. Figure provides an overview of our method. The following paragraphs describe the modules in more detail.
Monodepth backbone. The input image image encoder yeaa to produce 4 intermediate feature maps (f))ke = PPenc (I). As in Depth Pro (Bochkovski et al., 2025). we resize the input image so that H = W = 1536. We then feed the intermediate feature maps into the Depth Pro decoder Pdee produce monocular depth ((e (( Similar to Flynn et al. 22119), duplicate the last convolutional layer of the decoder to produce a two-channel depth map D 22x
R3xH*W fed into a pretrained Depth Pro
map D
One of our key observations is that depth is ill-defined and using frozen monodepth model degrade view synthesis fidelity, particularly for transparent or reflective surfaces (Wen et al.) 2025 During training, therefore unfreeze both Pdes and the low-resolution encoder part of Penc- This enables the full view synthesis training to adapt the depth prediction modules via backpropagation, in conjunction with downstream modules, for the end-to-end view synthesis objectives.
Depth adjustment. Although monocular depth estimation has made impressive advances recent years, the depth estimator still needs to deal with the inherent ambiguity of the task. In monocular depth estimation, the network might just resolve the problem by predicting outputs at the mean scale of possible outcomes (Poggi al.) 2020). When depth estimates are used for view synthesis, however, this ambiguity can lead to visual artifacts.
To address this, we take inspiration from the line of work on Conditional Variational Autoen- coders (C-VAE) (Sohn et al. 2015). which addresses the ambiguity by designing posterior model. In a traditional C-VAE, the posterior would take ground-truth depth D & as input and produce latent representation z. During training this latent vector would be passed through an information bottleneck in the form of a KL divergence. This ensures that the latent represents the smallest amount of information required to resolve the ambiguity of the task. We simplify this scheme and adapt it to our setting by interpreting z as a scale map E IR xW and replacing the KL divergence with task-specific regularizer. More details given in Section 3.4 The output of this module adjusted two-layer depth map D S(D. D.
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Gaussian initializer. We use this adjusted two-layer depth map R and the input image I E |3x HxW to initialize set of base Gaussians Go XX where H' H/2 and =V/.
To compute GI, respectively. This yields a downsampled depth map D' and input image I' We then unproject the resulting depth map D' produce mean vectors pu j) = li Di (i, j). D' (' j))²) Note that we deliberately do not use the intrinsics matrix of the input image here. This enables the network to reason about Gaussian attributes in normalized space without having to adapt its predictions to the field of view of the image. We set the scale proportional to depth: s(i.j) = (i with a fixed scale factor 8p- The color is initialized directly from the downsampled input image j(i, = ji. The rotation and opacity are initialized to unit quaternion [1, 0,0, 0)? and fixed value of 0.5, respectively.
first subsample I and D by factor of 2, using average and min- hin-ppooinn,
While the initial Gaussians provide reasonable starting point, they require substantial refinement to achieve high-fidelity rendering. The Gaussian decoder ifgauss takes as input the feature maps (fz (f.).) and the input image I, and outputs refinements AG E IR x2x xW for all Gaussian attributes:
Gaussian decoder.
AG
(1) for position A E R3x2xx'>XW scale AGmi E 1R xw" color AG E R3x2xH'*M
Pgguss ((fa) (I....
These refinements
include
deltas
AG E RR3x2xH'XX xW"
rotation
The ability to refine Gaussians across all attributes is crucial for creating a coherent 3D representation that models detailed geometry and appearance.
and opacity A alph E 1x22xf'x xw"
Gaussian composer. The Gaussian composer takes the base Gaussians Gy E <x2x"''>"" and
Gaussian refinements AG E RRx2x xW" as input and produces the final Gaussian attributes G R xWW' Instead of directly adding the values, we compose them with an attribute-specific activation function Vaaui
Gaur = Yatur
"Jatt (Tant (Go,,a)) + Tatt A AGuil
The supplement provides the details on the activation functions Yantr and scale factors Tattr-
Gaussian renderer. The resulting Gaussian representation can be rendered from arbitrary view- points using an in-house differentiable renderer R. The rendering process can be expressed as i= R(G, where i is the rendered image, G are the final Gaussian attributes, and P repre- sents the camera projection parameters for the desired viewpoint. Since we predict the Gaussians in normalized space, we would theoretically need to transform them using the extrinsics and intrinsics of the source view. However, we can alternatively incorporate this transformation directly into the projection matrix for the target view: P = E Eug K= where K and Egre are the intrinsic and extrinsic matrices of the source view, and Kigt and Eng are those of the target view. In contrast to image diffusion models, the inference cost is amortized: once 3D representation is synthesized, it can be rendered in real time from new viewpoints.
3.2 NETWORK ARCHITECTURE
Our architecture includes a number of trainable modules, illustrated in Figure The complete network has approximately 340M trainable parameters (702M parameters in total). It processes single 1536 1536 image and produces approximately 1.2 million Gaussians under one second on single GPU.
Feature encoder. We base our feature encoder on the Depth Pro backbone (Bochkovskii et al,, 2025). This encoder processes the input image E C=x and produces four feature maps (L)se(1...)) at different resolutions. The Depth Pro backbone consists of two Vision Transformers (ViTs) (Dosovitskiy al. 2021), one applied to a downscaled version of the input image and one applied to various image patches. The row-resolution image encoder and patch encoder each have 326M parameters. During training, unfreeze the low-resolution image encoder to allow adapta- tion to the view synthesis task, while keeping the patch encoder and normalization layers frozen to preserve the pretrained feature extraction capabilities.

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Depth decoder. Our depth decoder is based on the Dense Prediction Transformer (DPT) (Ranftl et al. 202I). We modify the original DPT decoder by duplicating the final convolutional layer to output two depth channels instead of one. Our decoder thus takes the feature maps (I....))
from the encoder and produces a two-layer depth map D E 2 xx The first layer represents the primary visible surfaces, while the second layer may represent occluded regions and view nee-dependent effects. The depth decoder consists of multiple convolutional blocks with approximately 20M pa- rameters. This module is fully unfrozen during training optimize depth prediction for view syn- thesis.
Gaussian decoder. The Gaussian decoder predicts refinements for all Gaussian attributes. It has the same DPT architecture as the depth decoder but we replace the last upsampling block with a custom prediction head. The decoder takes as imput the feature maps (I....) the input image I, and the predicted depth maps D. It outputs tensor AG E R x2xx2x xW" that contains deltas for all Gaussian attributes: position (3 channels), scale (3 channels), rotation (4 channels), color (3 channels), and opacity (I channel). This decoder has approximately 7.8M parameters and is trained from scratch. The high dimensionality the output (approximately 16.5M values) enables fine-grained control over the Gaussian representation.
Depth adjustment. For the depth adjustment network we use a small U-Net (Ronneherger et al, 2015) with 2M parameters that takes both the predicted inverse depth D-1 and the corresponding ground truth D inputs and produces a scale map S E R#*W, During inference replace the depth adjustment module with the identity function.
3.3 TRAINING STRATEGY
Supervision. We supervise predicted 3D Gaussians in image space through differentiable rendering. Each training sample consists of two views: the input view and the novel view. We predict Gaussians from the imput view, render in both views, and evaluate losses on these renderings. The losses are defined in Section 3.4
We
two-stage curriculum.
Stage 1: Synthetic training. We first train on synthetic data with perfect image and depth ground truth for both the input view and the novel view, allowing the network to learn fundamental principles of 3D reconstruction without real-world ambiguities. The synthetic data is further described in the supplement.
Stage 2: Self-supervised finetuning (SSFT). We fine-tune the model on real images that have no ground truth for view synthesis. To this end, we use our trained model generate pseudo ground truth on single-view real images from OpenSSeene (2023) and online resources, detailed in the supplement. For each real image, we generate 3D Gaussian representation and render a pseudo- novel view. We then use the pseudo-novel view as the input view, and the real input image as the novel view. The swapping input and novel views forces the network to adapt to real images, enhancing its ability to generate coherent novel views.
Unlike AdaMP1 (Han et al. 2022), which constructs stereo pairs from single-view collections using warp-back strategy, our approach leverages the 3D representation generated by our model to create pseudo-novel views. This maintains geometric consistency while adapting to real images without requiring stereo pairs.
3.4 TRAINING OBJECTIVES
We train network using combination of loss functions:
Rendering losses. We apply an L1 loss between the rendered image î and the ground truth I on both input and novel views:
Leolor
Σ
Epnst [Nriew Iview (1))
(3)
viewe (inpal, novel)
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where S2 denotes the set of all pixels p. We further perceptual loss (Johnson et al. et al. 2016 Suvorov et al. 2022) on novel views to encourage plausible inpainting:
2016
Gatys
pprcop
fear [o(((nvee)) Ør (Ioove))
+ Xa
Gram
Mi - Mi (Inoel)))
(4)
where 61 and Mj are the 1-th layer of our feature extractor and its Gram matrix, respectively. We apply a Binary Cross Entropy (BCE) loss to penalize rendered alpha on the input view discourage spurious transparent pixels:
Calpha
Σ
Exnn [CBCE(A (p), (p),
(5)
viewE (inpu, novel)
where Aview is the rendered alpha image.
Depth losses. We apply Ll loss between the predicted and ground-truth disparity, only the input view, exclusively on the first depth layer:
Caepth = Ep-
(6)
where ((1) and D the first predicted depth layer and the ground-truth depth, respectively.
Regularizers. We apply a total variation regularizer on the second depth layer to promote smooth- ness:
C Ep-ss
V (2) (p)1 + (2)
where ((2) is the second predicted depth layer. Additionally, we apply a regularizer to suppress floaters with large disparity gradients:
(1-oxp exp (-1 max {0, VD ((GG)))))))
Cgrad = EsaI Gulpha (i)
(8)
where Z is the index set for the Gaussians and (-) computes the projection of the Gaussian position onto the 2D image plane. We use c 10-2 We further constrain Gaussian offset magnituees AG AGy to discourage extreme deviations from the base Gaussians:
Casta EsI [max AG (i) - 6, 0} + max g - 8, 0H},
(9)
with = 400.0. In screen space, we regularize the variance of projected Gaussians:
Cyplal = E (aaxx(o(G())) o mux 0} + max T o (G)))).03).
(10)
where (-) computes the projected Gaussian variance and a 10 max 102.
Depth adjustment. We regularize the depth adjustment with an MAE loss and a multiscale total variation regularizer:
Lscale Ep [[S(p) 1]] and L Σ Ba [[VS
(11)
Here Sif denotes scale map downsampled by factor 2 on the downsampled image domain Qia- The depth adjustment losses as an information bottleneck, encouraging the network to learn the most compact representation to resolve depth ambiguities.
The final loss composition of all the loss terms:
C - ArLa + M.C. M
(12)
dED
FER
SES
where D = {color, alpha, depth, percep), R {tv, grad, delta, splat), S (scale, Vscale} the attribute sets for the data terms and regularizers. The hyperparameters are specified in the sup- plement.
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4 EXPERIMENTS
We first train our model for 100K steps on 128 A100 GPÜs using synthetic data only (Stage 1). We then fine-tune our model using self-supervision for 60K steps on 32 A 100 GPUs (Stage 2).
Datasets.
We evaluate our approach on multiple datasets with metric poses: Middlebury (Scharstein
et al. 2014), Booster (Ramirez et al. 2024), ScanNet++ (Yeshwanth et al., 20.3), WildRGBD (iia al. 2024). ETH3D (Schöps et al. 2017). and Tanks and Temples (Knapitsch al., 2017). The sampling choices are discussed in the supplement. We do not include non-metric datasets such as RealEstate (Zhou al. 2018).
Evaluation metries. We employ LPIPS (Zhang et al. 2018) and DISTS (Ding et al., 2022) to quantitatively assess the quality of novel view synthesis. We focus primarily on these perceptual metrics, since older pointwise metrics such as PSNR and SSIM can be overly sensitive to small translations, where even a 1% shift can lead catastrophic drops in scores despite visually similar results. (See the supplement for an illustration. We also list PSNR and SSIM numbers in the supplement for completeness.) Since we are interested in sharp high-resolution view synthesis, we evaluate all methods the full-resolution ground truth. If method generates results at lower resolution, we resize the output image to the input resolution before evaluation. a method crops the input and generates cropped results (Ren et al. 2025 Yu et al., (025b) Mi et al. 2025 Zhou
et al. 2025), we evaluate against correspondingly cropped ground-truth images.
Baselines. We compare SHARP the following state-of-the-art methods: Flash3D (Szymanowic7 et al., 2025a), which is based on 3D Gaussians; TMPI (Khan et al.] [2023), which uses multi-plane images; LVSM (Jin et al. (025), which is based on image-to-image regression; and Stable Virtual
Camera (SVC) (Zhou et al. 2002),, ViewCrafter (Yu et al. 2025b). and Gen3C (Ren et al. 2025)
which employ diffusion models.
Quantitative evaluation. Table I presents a quantitative evaluation of SHARP and the baselines in the zero-shot regime. (Cross-dataset generalization to datasets that were not used during training.) For each metric, we report the mean value over all test samples. SHARP achieves the highest accuracy on all metrics across all datasets. Additional experimental results are provided in the supplement.
Table I: Quantitative evaluation. Lower is better. column are highlighted.
Best
second-best and third-best in each
Middlebury
Boester
ScanNetet
WIIdRGBD
Tunks and Temples LPIPS
ETH3D
DISTSL
LPIPS.
DISTS.
LPIPS|
DISTS.
LPIPS]
DISTS]
LPIPSL
DISTS.
DISTS.
LPIPS,
Plash3D
0.359
0.581
0.409
0.370
0.374
0.572
0.159
0.345
0.382
0.683
0.538
0.651
TMPI
0,858
0:436
0.232
0.409
0.128
0.309
0.114
0.327
0.309
0.693
0.396
0.720
LVSM
0.274
0.355
0.307
0.404
0.145
0.302
0.095
0:257
0.227
0.575
0.555
0.664
SVC
0.208
0.629
0.283
0.448
0.201
0.596
0.157
0.531
0.230
0.733
0.420
0.708
ViewCritter
0.373
0.751
0.318
0.523
0.176
0.526
0.148
0.386
0.295
0.759
0.454
0.748
Gen3C
0.164
0.545
0.207
0.384
0.090
0.227
0.106
0.285
0.177
0.366
0.408
0.734
SHARP (ours)
0.097
0.358
0.119
0.270
0.071
0.154
0.069
0.190
0.122
0.421
0.258
0.554
Qualitative results. Sshows novel views synthesized by SHARP and a number of baselines. Additional qualitative results, including on images from all evaluation datasets, can be found in the supplement. SHARP consistently produces higher-fidelity renderings from nearby views.
Ablation studies. We conduct extensive ablation studies and controlled experiments the losses, training curriculum, depth adjustment, and more. The perceptual loss brings substantial improve- ment in visual quality, while the regularizers address some classes of artifacts. The learned depth adjustment boosts image sharpness and enhances details. The SSFT likewise yields crisper synthe- sized views. Detailed results and sample images are provided in the supplement.
CONCLUSION
We presented SHARP, an approach to real-time photorealistic rendering of nearby views from a single photograph. SHARP synthesizes 3D Gaussian representation via single forward pass through a neural network in less than a second on a standard GPU. This 3D representation can then be rendered in real time high resolution from nearby views. Our experiments demonstrate

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that SHARP delivers oftee-of-the-art image fidelity for nearby view synthesis, outperforming recent approaches that are in some cases two to three orders of magnitude more computationally intensive.
One clear opportunity for future work is extend the methodology to support photorealistic syn- thesis of faraway views without compromising the fidelity nearby views or the benefits of fast interactive synthesis. This may call for judicious integration of diffusion models (Po et al. 2024), possibly with the aid distillation for reducing synthesis latency (Yin et al. 2024). With diffusion models, unified view synthesis routine for single-view, multi-view, and video input (Ren et al.) 2025 Ma et al. 2025) may emerge as a versatile generalization. Another interesting avenue is principled treatment of view-dependent and volumetric effects (Verbin et al. 2024).
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SUPPLEMENTARY MATERIAL
A IMPLEMENTATION DETAILS
A.1 ATTRIBUTE SPECIFIC ACTIVATION
Activation functions and their corresponding scale factors 17 in Eq. 2
are specified below:
position (x/z,u/²)
position (2-1)
color
rotation
scale
alpha
identity
softplus 10-3
sigmoid 10-1
identity
sigmoid
sigmoid
10
For the position, apply the activation function in NDC space, i.e. first map [2, y, 2] - [z/2, 9/2, 1/2] before applying the activation function and adding the delta. After the operation, we transform the result back to world coordinates.
A.2 TRAINING OBJECTIVES
In the loss configuration, we choose Acolor = 1.0, Aulpha = 1.0, Apereep 3.0,
Adepth 0.2, Apv = 1.0,
Agrad 0.5, Adela 1.0, Aaplat 1.0, Ascale 0.1, A 5.0 in Eq. 12
For the perceptual loss in Eq. 4 we use Xfeat D. and X AGram denotes the shape of the 1-th feature map (x(-) E D...
where D1 Hi Wi
D?
We trained the network using the Adam optimizer Kingma & Ba 2015) with cosine learning
rate schedule (Loshchilov Hutter 2017). The learning rate was linerlly warmed up for 10,000 iterations to an initial value of 1.6 x 10 after which it decayed to final value of 1.6 x 10
A.3 VIEW FRUSTUM MASKING
We implement a view frustum masking technique to address ambiguity in view synthesis, since regions occluded in the original view have multiple plausible reconstructions. By using depth infor- mation to determine which regions in the new view correspond to points visible in the original view, apply supervision only where ground truth is reliable.
To calculate this mask, we project points from the target view back to the source view:
o''''.u 2', 2', somel
( 2,,,,,,2,11
(13)
The mask is then defined
1, if - 1.05 Sx S 1.05 and -1.55 y S 1.05
M ./)
(14)
0, otherwise
This mask is applied to all image-based losses on the target view.
A.4 THE PERCEPTUAL Loss
Here we detail the challenges and our solutions incorporating the perceptual loss. We employ the perceptual loss aimed improving inpainting (Suvorov et al. (2022). Similar to its application in the image domain, we initially applied the loss only in the occluded image patches of the novel view; however, through experiments, observed that applying the loss to the entire rendered image resulted more plausible details and fewer artifacts in general, even in the non-occluded foreground regions, seen in Figure
However, this formulation of loss imposes two major challenges: (a) heavy memory overhead, and (b) compromised sharpness.
Memory. The perceptual loss maximizes feature similarity between the rendering and the ground truth. It is constructed from combination of MSE losses layer-wise feature maps of deep neural
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networks (in our case, ResNet-50). Since the loss itself is computed through deep neural network, when applied to full images, adds a significant memory overhead to the already large computation graph during backpropagation. Furthermore, when the loss is applied to both the reconstruction and synthesized views, the accumulated computation graph can lead to out-of-memory conditions even on an A 100 with generous memory pool (40GB) with batch size of one.
To address the problem, one potential workaround would be simply reducing the activation precision BF16; however, this does not address the fundamental problem of computation graph accumula- tion, prevents scaling the loss to more novel view supervisions, and causes training instability, es- pecially when predicted 3DGS contains properties (e.g., singular values) that are prone precision changes. Gradient checkpointing is another option, but it can drastically impair training efficiency.
To address this problem, we propose a novel computation graph surgery mechanism. We implement a surgery operator to accept and cache gradients along with the inputs during the forward pass, and to inject eached gradients during the backward pass. Then, at the perceptual loss node in the graph, we eagerly pre-compute the gradients with respect to the features via an explicit autograd call, release the partial computation graph involving ResNet, and override the node with the surgery- operated one. This strategy avoids accumulating the computation graph and leads to compact graph that is agnostic to the number of pixels or views. As a result, we are able to continue training at the full FP32 precision with perceptual loss both reconstruction and novel views, without compromising training throughput. It is worth noting that the surgery operator is a general operator and can be integrated into any training framework with similar memory concerns regarding the computation graph.
Sharpness. Since the perceptual loss is applied the latent feature space, while it offers the ben- efit of more plausible inpainting, the output renderings often tend to be blurry in the pixel space. Through backpropagation, this translates to large and blobby 3D Gaussians, whose renderings are simultaneously less detailed and more cone-consuming.
To encourage sharpness, explored losses that reduce feature space distance and revived the Gram matrix loss (Reda et al. 2022) that was originally designed for style transfer. This loss matches the auto-correlation of the latent features, further enhancing feature space similarity and boosting image sharpness. We introduce this loss in Eq. 4 As mentioned above, the original Gram matrix loss was applied to VGG features targeted at style transfer, and cannot be directly transferred to the Re features pre-trained for inpainting. We conducted series of controlled experiments
& with 2 = Dz-J E (1, 10, 100, 500), along with Afeat D, D. h E (0.1, 0.3, 1.0, 10.0), and identified the most promising combination, as reported in Section through extensive quantitative metric validation and qualitative human inspection. This carefully tuned perceptual loss improves the DISTS metrics by 62% and 47% on benchmarks (as seen in Table 8), and reduces rendering latency by 49% and 36% respectively (Table 9).
B TRAINING DATA
B. SYNTHETIC DATA
In Stage of training (Section 3.3 we use a large-scale synthetic dataset generated using an in- house procedural content generation system. This system operates by sampling from a large col- lection of artist-made environments, comprising over 2K outdoor and 5K indoor scenes, and aug- menting them procedurally. For each sampled environment, the framework populates the scene with high-quality digital human characters featuring realistic hair grooms and garments, along with va- riety of additional objects. This approach enhances the structural and visual diversity of the dataset while preserving the underlying artistic quality of the base environments.
To further enhance scene diversity and complexity, the framework supports random placement of various object types, including thin structures, transparent materials, and reflective surfaces, across a wide range of spatial configurations. It also offers fine-grained control over camera parameters such position, orientation, and focal length, as well detailed illumination settings. Lighting setups include physically-baie based direct light sources, with variations in direction, intensity, and color temperature. We also use -yggihyaamic-range (HDR) environment maps, which are sampled from
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curated collection of high-resolution HDRIs. This combination enables realistic global illumination effects under diverse and physically plausible lighting conditions.
For each scene, we identify object of interest and position ring containing 10 virtual cam- cras around it at varying distances and angles. The cameras are arranged in concentric circles such that the cameras the dataset to capture the same object or scene element from diverse perspectives. All images are rendered using the V-Ray physically based rendering engine, ensuring photorealistic lighting and material interactions. The final dataset consists of approximately 700K unique rendered scene in- stances, each with 11 rendered views, totaling around 8M images 1536 1536 or 2048 x 2048 resolutions.
no more than 60 cm apart, simulating multi- capture setup. This allows
B.2 REAL- WORLD DATA
In Stage of training (Section 3.3 we use OseeOnenee (2023) as well as collection of high-quality photographs from Shutterstock, Getty Images, and Flickr, all with commercial licenses. The dataset. contains 2.65M images in total.
C EXPLANATORY FIGURES
C.I IMAGE FIDELITY METRICS
To determine which metrics are most suitable for evaluating view synthesis quality, we conducted an experiment analyzing how different metrics respond to simple image translations.
observe that older pointwise metrics such PSNR and SSIM are highly sensitive to small spatial misalignments. mere 1% translation causes PSNR to drop to 11.2 and SSIM to 0.375, values that are surprisingly close to those obtained when comparing with mean image (PSNR 10.7, SSIM 0.351).
As shown in Table 2 and Figure
(a) Reference image
(b) Translated 1%
(c) Mean image
Figure 4: Effect of small translations metrics. A 1% translation (b) of the reference image (a) appears nearly identical to human observers, yet dramatically affects PSNR and SSIM values. The mean image (c), despite being unrecognizable compared to the reference, produces PSNR and SSIM values remarkably similar to those of the 1% translation.
Perceptual metrics, such DISTS and LPIPS, demonstrate better robustness to these small trans- lations. DISTS shows exceptional stability with a value of 0.079 for a 1% translation compared to 0.859 for the mean image. This characteristic is especially relevant for evaluating view synthesis, where geometric inaccuracies can manifest as small shifts between synthesized and ground truth views. Since novel view synthesis must address both geometric and appearance errors, metries that
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Table 2: Sensitivity of metrics to small image shifts. Different metrics exhibit varying sensitivity to small spatial misalignmentss
DISTS!
LPIPS
PSNRT
SSIM
Comparison
Translated (0. 1))
0.008
0.059
21.3
0.623
Translated (1.0%)
0.079
0.491
11.2
0.375
Translated (5.0%)
0.121
0.723
8.1
0.249
Mean Image
0.859
0.970
10.7
0.351
can accommodate minor geometric misalignments while still reflecting perceptual quality provide evaluations that correspond more closely to human perception. Based on these findings, we adopted DISTS and LPIPS as our primary evaluation metries.
C.2 DEPTH ESTIMATION UNCERTAINTY
Monocular depth estimation is fundamentally ill-posed, multiple 3D configurations can produce the same 2D image (Poggi et al.) 2020). Siguutaiillsstaaeess this ambiguity by comparing depth pre- dictions for image and mirror image, technique similar to that used in left-right consistency for monocular depth training (Godard al., 2017). The uncertainty map reveals that depth esti- mators struggle most at object boundaries and in regions with complex geometric structures, such as foliage. When these ambiguous depth estimates are used directly for view synthesis, the result- ing images can exhibit visual artifacts as the network attempts to average across multiple plausible depth configurations. Our depth adjustment module, inspired by Conditional Variational Autoen- coders (Sohn et al. 2015), addresses this issue by learning a scale map that refines the predicted depth during training, addressing these ambiguities in way that optimizes for view synthesis qual- ity rather than depth accuracy alone.
(a) Original image
(b) Predicted depth
Flipped prediction
(d) Uncertainty map
Figure 5: Ambiguity in depth estimation. We demonstrate the inherent ambiguity in monocular depth estimation by (a) taking an original image, (b) predicting its depth using Depth Pro, (c) hori- zontally flipping the image. applying Depth Pro. and flipping the result back, and (d) computing the relative absolute error between the two predictions to generate an uncertainty map. Higher values (brighter regions) indicate greater inconsistency between predictions.
D EXPERIMENTS
D.1 EVALUATION DATASET SETUP
For stereo datasets (Middlebury, Booster), apply SHARP and the baselines the left frame and predict the right frame.
For multi-view datasets (ScanNet++, WildRGBD, Tanks and Temples, ETH3D), proceed as follows:
For each sequence/scene,, we split them into 10-view sets.
Within each 10-view set, we compute pairwise depth overlap and select pairs with overlap 60%. For datasets with sparse depth (e.g., ETH3D), predict monodepth via Depth Pro (Bochkovskii et al., 2025), apply global scale alignment from dense monodepth to sparse depth (Eq. 16), and compute the monodepth overlap.
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We select mi1(51 #pairs) pairs to evaluate per dataset. For each pair, predict target image from the source image.
The reason for limiting the number of pairs the slow inference speed of diffusion-basse baselines. For instance, Gen3C takes 15 minutes synthesize new view (as byproduct of synthesizing video). 512 pairs already take roughly 5 days to evaluate on an A100; any larger set becomes less tractable evaluate.
Figure shows the distribution of pairwise camera baseline size across datasets.
Booster
HI
Middlebury
u 0-02133
Count
Count
0508
Co023
62624
033G3
2.1
6.2
825
2.38
Baseline (meted)
Beseline (meier) TanksAAdfTemples
Baseline (meter)
ScanNet-t
WildRGBD
(n-512 u-0.127, ow0.228)
(n-152, p-0.373, ow0.358)
(n-512, p-0.228 cw0)
Chunt
Chunt
Count
1.55
Baseline (mutter
Busalline (mrater))
(marbe))
Figure 6: The pairwise camera baseline size distribution across datasets.
ScanNet++. We sample from the uvs test split with DSLR images.
WildRGBD. We sample from the validation split from mvs list in each scene.
Tanks and Temples. We take the training set for Tanks And Temples. We composite SfM poses and SiM-to-LiDAR transformation, both provided by the authors, to create camera matrices in the metric space, then backproject the LiDAR points the associated images to form ground truth depth maps. The depth maps were only used for experiments with privileged depth information.
ETH3D. We use the ETH3D high-resolution multi-view training set. As with Tanks and Temples, we only use sparse depth for experiments with privileged depth information.
D.2 BASELINES
We report model sizes of SHARP and baselines in Table B The numbers based reported numbers in the publication and source code. The original TMPI paper utilizes DPT depth (Ranftl et al. T)2T)aaa the monodepth backbone; replace it with the latest Depth Pro (Bochkovskii al. for better quality and hence report the total parameters with Depth Pro backbone.
2025
Table Parameter counts across models. Trainable parameters are estimated by subtracting frozen module parameter counts from total counts. *: finetuning diffusion models.
Flash3D
TMPI
LVSM
SVC
ViewCrafter
Gen3C
SHARP (ours)
# total
399M
957M
314M
2.33B
3.17B
7.7B
702M
# trainable
52M
6M
314M
1.26B*
2.6B*
7.4B*
340M
To verify that the in-house synthetic data (Section B
not the dominant factor in the view synthesis fidelity demonstrated by SHARP, retrain Flash3D the same in-house synthetic data.
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We trained 24K (3%) and 216K (28%) scenes from data for 100K steps and 150K steps, respectively. We do not further scale up the number of scenes because we do not find a consistent positive signal of scaling data with Flash3D, and more scenes trigger data loader crashes in the reference implementation As shown in Table 4 we do not observe a distinct improvement when training Flash3D with our synthetic data. This implies that in-house data quality is not the principal factor in the reported view synthesis performance.
Table 4: Training Flash3D on in-house synthetic data.
data
Middlehury
Rooster
Nean
WildRGHID
Tanks and Temples
ETH52
DISTSL
LPIPS|
DISTS]
LPIPS]
DISTSL
LPIPSL
DISTSI
LPPSS
DISTS.
LPIPS]
DISTSI
LPIPSL
(356)
0.599
03335
0.403
0.417
0.506
0.673
internal (28%)
0.433
0.647
0.442
0.415
0.488
0.696
0.255
0.448
0.553
0.815
0.570
0.686
public (KF10K)
0.359
0:581
0.409
0,370
0.374
0572
1.959
0.345
0.382
0.683
0,535
01651
D.3 ADDITIONAL QUANTITATIVE EXPERIMENTS
PSNR and SSIM. For completeness, we report PSNR and SSIM Table 5 use for evaluating view synthesis fidelity, per the analysis in Section C.I
but we discourage their
Table 5: We report PSNR/SSIM metries for completeness. See Section metrics.
C
for analysis of the
Midddehury
Boaster
ScanNet++
WildRGBD
Tanks and Temples
ETHED
PSNRT
SSiMt
PSNR1
SSIM
PSNRR
SSIM1
PSNR1
SSIM
PSNR1
SSIM1
PSNR1
SSIM
Flash3D
15.88
0.683
22.40
0.873
18.14
0.641
18.09
0.616
15.80
0.518
15:21
0.682
TMPE
16.42
0.688
19.44
0.833
16.16
0.712
16.44
0.559
12.41
0.368
12.61
0.540
LVSM
15.53
0.681
20.16
0.843
20.25
0.775
18.04
0.594
15.95
0.519
16.72
0.722
SVC
12.72
0.613
17.65
0.781
11.71
0.624
12.20
0.410
11.76
0.413
13.36
0.662
ViewCrafter
10.33
0.569
0.692
13.30
0.645
14.43
0.437
11.49
0,423
11.94
0.621
Gen3C
13.89
0.624
20.19
0.837
20.82
0.792
16.54
0.504
14.83
0.499
13.09
0.642
SHARP (ours)
17.12
0.693
22.19
0.864
22.63
0.833
19.57
0.655
16.33
0.528
14.51
0.610
Runtime. Runtimes are reported in T SHARP synthesizes the 3D representation in less than a second on A100 GPU. The representation can then be rendered in real time (100 FPS higher on most datasets). We always render the results to the native resolution of the datasets, which explains the variability between datasets (e.g. ETH3D has native resolution 6048 x 4032).
Table 6: Runtime (in seconds) on an A100 GPU. Note that the SVC/TMPI runtime is lower on ETH3D, since they encountered memory issues and we had to rerun them on an H100.
Middiclury
Booster
ScanNotee
WIKIKGBD
Taoks Inference]
Temples Render]
ETH3D
Infermncel
Render]
Inference]
Render]
Infermnce]
Render]
Infreence.
Remler]
Informmce]
Render]
Flask3D)
0.154
0.025
0.154
0.047
0.155
0.004
0.154
0.001
0.154
0.004
0.153
0.041
TMPL
1.328
0.289
0,333
0,248
0.315
0.247
0.183
0.294
0,272
0.218
0.222
0.157
LYSM
0.121
0.120
0.120
0.120
0.120
0.121
SVC
62.687
$7.598
62,6711
$7.456
TH.846
32.610
Crafeer
[19.7
118.679
119.385
119.590
119.859
119.922
CGn3C
830.225
831.775
836-455
838.695
$41.488
838. 143
SHARP (ours)
0.912
0.010
0.911
0.016
0.911
0.006
0.912
0.004
0.912
0.005
0,910
0.022
D.4 EVALUATION WITH PRIVILEGED DEPTH INFORMATION
Table 7 evaluates all view synthesis methods when privileged ground-truth depth maps are used for scale adjustment. We again report PSNR/SSIM metrics for completeness but discourage their use for view synthesis fidelity.
For approaches where a depth proxy is available (Flash3D uses UniDepth (Piccinelli et al. 2024
ViewCrafter uses Dust3r (Wang al. 2024). TMPI uses DepthPro (Bochkovskii al. 2025
Gen3C uses MoGe (Wang et al., 2025). and SHARP uses DepthPro (Bochkovskii et al. 2025
we align the intermediate depth representation D to the ground truth D to derive an approximate
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global scale factor:
D(p)
8 = median
(15)
D(p)
D(p) - D(p).
(16)
For other approaches (LVSM and SVC), for each pair, we apply a linear scale sweep to find the best scale that minimizes the DISTS score.
Table View synthesis fidelity with privileged depth information.
Middiebury
ScanNete+ DISTS,
WildRGBD DISTS!
Tanks DISTS.
Temples LPIPS]
ETH3D
DISTS)
LPIPS.
DISTS,
LPIPS]
LPIPS]
LPIPS,
DISTS.
LPIPS]
Hash3D
0.333
0.510
0,412
0.361
0.283
0.395
0.181
0.368
0.399
0.666
0.474
0.595
0.356
0.736
LVSM
0.243
0.564
0.294
0.428
0.125
0.236
0.088
0.229
0.219
0.558
0.456
0.668
SVC
0.181
0.518
0.257
0.381
0.146
0,459
0.120
0.447
0.199
0.653
0.410
0,700
ViewCrafter
Gen3C
0.124
0.347
0.192
0.291
0.085
0.196
0.078
0.118
0.149
0.434
0.283
0.368
SHARP (ours)
D.081
0.262
0,110
0.214
0.068
0.137
DO07
0.117
0.112
0.374
0.187
0.381
Middlebury
Booster
ScanNote#
WidRGBD
Tanks and Temples
ETH3D
PSNR
SSIM1
PSNR1
SSIM
PSNR
SSIM1
PSNR1
SSIM1
PSNR1
SSIV1
PSNR1
SSIM1
Flash3))
18:61
0.719
23.16
0.879
21.98
0.803
19.45
0.679
16.40
0.567
17.06
0.674
TMPE
17.54
LVSM
15.22
0.672
19.27
0,823
23.42
0.826
19.29
0.627
16.31
0.529
16.38
0.719
15.73
0.671
20.27
0.841
14.77
0.483
13.47
0,465
ViewCrafter
17.11
0.703
21.55
0.860
19.73
0.788
20:10
0.672
16.84
0.506
18.81
0:721
Gen3C
18.46
0.720
23.12
0.875
22.11
0.822
22.45
0.745
17.23
0.557
18.93
0.216
SHARP (ours)
19.18
0.742
23.57
0.880
23.67
0.865
23.62
0.780
16.92
0.543
19:09
0.715
D.5 ABLATION STUDIES
We summarize the results from extensive ablation studies in Tables 13 and Figures 9
Datasets. We report metrics on ScanNet++ (small-scale scenes) and Tanks and Temples (large-scale scenes), and display results on the real-world dataset Unsplash (Unsplash 2022).
Models. For losses, depth adjustment, and unfreezing experiments, train multiple variants of our model for 60K steps on 32 A100 GPUs only on Stage without Stage 2 SSFT. For the SSFT experiment, compare Stage and Stage models discussed in Section of the main paper.
Losses. We always incorporate color and alpha losses for appearance reconstruction. Our ablation of loss terms (Table (tableBaand and Figure/9) shows that the depth loss reduces geometry distortion, and per- ceptual loss brings significant improvement in inpainting quality and image sharpness; both losses result in improved metrics. While our regularizers do not move the metrics the datasets used for ablation analysis, they qualitatively improve scenes with challenging geometry and faraway back- grounds (Figure D). We also observe that our regularizers boost rendering speed (Table which attribute to the fact that they prevent degenerate or very large Gaussians.
Because of the importance of the perceptual loss, we separately evaluated the performance improve- ments from the Gram matrix component (Table 10). Our results show that adding the Gram Gram-matrix matrix loss significantly improves results.
Table 8: Ablation study on loss components. The perceptual loss significantly enhances image
quality; regularizer losses (Lreg e ETER X, f,r in Eq. 2 do not have a strong effect on the metrics
but yield qualitative improvements. (See Figure
ScanNet++
Tanks and Temples PSNRT
Leckee + Carpa
Leegth
Egencep
Lreg
DISTS]
LPIPS]
PSNKT
SSIM
DISTS_
I.PPS_
SSIMT
X
X
0.229
0.414
18.18
0.768
0.301
0.656
14.75
0.520
x
0.162
0.270
22.95
0.844
0.239
0.548
16.23
0.550
0.063
0.143
23.65
0.843
0.126
0.421
16.29
0,531
/
0.064
0.147
22.61
0.829
0.126
0.419
16.19
0.523
Depth Adjustment. Table evaluates the contribution of learned depth adjustment during training. The depth adjustment consistently improves perceptual image fidelity metrics. This can also be
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Table 9: Effect of loss terms on rendering speed. Median rendering latency per frame for different loss combinations. Loss terms improve rendering speed.
ScanNet++
Tanks and Temples Latency.
Lealor + Lalpha
Edepth
pprccep
Leg
Latency
x
X
x
22.2 ms
15.5 ms
/
x
X
12.2
8.8 ms
x
6.2 ms
5.6 ms
/
V
5.5
4.9 ms
Table 10: Ablation study on perceptual loss.
Adding the Gram matrix loss improves performance.
ScanNet++
Tanks and Temples LPIPS.
Gram loss
DISTS
LPIPS|
PSNR1
SSIM
DISTS!
PSNR1
SSIM1
0.070
0.153
22.26
0.827
0.130
0.441
15.89
0.517
0.064
0.147
22.61
0.829
0.127
0.420
16.19
0.522
in the qualitative examples in Figure 10 where the of the depth adjustment during training yields model that synthesizes sharper views.
Table 11:
Ablation study depth adjustment. Using the learned depth adjustment module consis-
tently improves image quality. See also Figure I0
ScanNet++
Tanks and Temples
Learned
DISTSL
LPIPS
PSNRT
SSIM
DISTSL
LPIPSL
PSNRT
SSIM
x
0.077
0.154
22.89
0.838
0.148
0.444
16.04
0.519
0.064
0.147
22.61
0.829
0.126
0.419
16.19
0.523
12 evaluates the contribution of self-supervised fine-tuning on real images (Stage 2 in Section 3.3 The metrics the ablation datasets par, but qualitative analysis in Figure IT indicates that self-supervised fine-tuning yields sharper images. We hypothe- size that these improvements due the limited presence of complex view-dependent effects in synthetic data.
Self-supervised Fine-tuning. Table
Table 12: Ablation study on self-supervised fine-tuning, While SSFT does not yield consistent metric improvement across datasets, we found it helpful qualitative studies. (See Figure I1]
ScanNel++
Tanks and Temples
SSL
DISTS!
LPIPS.
PSNRT
SSIMt
DISTS!
LPIPS.
PSNR1
SSIMT
x
0.063
0.142
22.86
0.835
0.125
0.433
15.91
0.513
0.071
0.154
22.63
0.833
0.122
0.421
16.33
0.528
Unfreezing Backbone. Unfreezing the monodepth backbone improves view synthesis fidelity, both quantitatively (Table [3) and qualitatively (Figure 12). Qualitatively, we observe that unfreezing the monodepth backbone resolves boundary artifacts, improves reflections, and resolves artifacts in scenes with challenging geometry.
Number of Gaussians. Table 14 evaluates the contribution of the number of Gaussians that output from our network. We compare the full x 768 x 768 > 1.2M output to 2x and 4x downsampled output. We see that performance of our method improves when predict more Gaussians. This is confirmed by our qualitative results in Figure [3
D.6 MOTION RANGE
While SHARP excels at generating high-quality nearby views (e.g. for AR/VR applications). was not designed for synthesis of faraway views that have little overlap with the source image.
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Table 13: Ablation study on unfreezing the monodepth backbone. See also Figure 12
ScanNet++
Tanks Temples LPIPS.
Unfreeze
DISTS
LPIPS!
PSNR
SSIM
DISTSI
PSNRT
SSIM
0.084
0.158
22.21
0.833
0.139
0.434
15.83
0.506
0.064
0.147
22.61
0.829
0.126
0.419
16.19
0.523
Table 14: Ablation study on number of predicted Gaussians. improves performance. See also Figure 13
Increasing the number of Gaussians
ScanNet++
Tanks and Temples
Gaussians
DISTS!
LPIPS
PSNR
SSIM
DISTSJ
LPIPS
PSNR
SSIM
2 192 192
0.110
0.199
20.46
0.799
0.181
0.458
16.27
0.525
2 x 384 x 384
0.077
0.160
22.00
0.822
0.140
0.425
16.23
0.525
2 x 768 768
0.064
0.147
22.61
0.829
0.126
0.419
16.19
0.523
In Figure 7 we study the perceptual metrics trend against the motion values (measured by pairwise camera baseline size meters) in our evaluation setup (see Section D.T). Experiments show that while SHARP works well, expected, small camera motion (< 0.5 meters), it retains its quality on larger motion and performs better than most other approaches on extended motion ranges. The SOTA diffusion-based approach Gen3C only outperforms SHARP ETH3D with motion meters and on ScanNet++ with motion > 0.5 meters. We also see that with privileged info, the quality regression over motion range can be further alleviated. In summary, quantitative analysis shows that while the desired motion range is around half a meter, our approach still works reasonably well on larger camera displacement.
In Figure 14 we deliberately extend the range of motion beyond SHARP's intended operating regime. To ensure comparable visual quality of baselines, we provide monodepth from Depth Pro as privileged depth information to all methods in this analysis. Per discussion in Section D.4, we do not show SVC and LVSM results in Figure since they cannot make use of privileged information and cannot perform scale sweep due to lack of ground truth novel view.
Qualitatively, we see that extending the range of motion reduces image fidelity in all regression- based approaches. On the other hand, diffusion-based approaches such as Gen3C can synthesize content even for far-away views. However, we also observe the tendency by diffusion models to alter the content of the image even for nearby views (e.g., the stirrups and horse's tail in Figure 14).
We believe it is an interesting research direction to combine the strengths of diffusion- based ap- proaches (synthesis of faraway content) and feedforward models such SHARP (interactive gen- eration 3D representation that can be rendered in real time).
D.7 FAILURE CASES
Apart from the failure excessive motion ranges that exceed the operation domain, like all machine learning models, SHARP may fail under challenging scenarios. In Figure & we show several such examples.
In macro photo, due to strong depth-of-field effect, the bee's depth is incorrectly inter- preted behind the flowers, leading to detached wings and distorted tail in novel view synthesis.
Due to the rich starry texture in night photo, the sky is interpreted a curvy surface instead of a plain surface far away, causing heavily distorted rendering.
The complex reflection water is interpreted by the network as a distant mountain; there- fore, the water surface appears broken.
These failures root caused by the depth model, and despite unfreezing the depth backbone, SHARP is unable to recover from the corrupted initialization. We regard this as a
long-tail tail problem of depth prediction. Retraining the depth backbone with higher capacity through more data may
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Booster
ETH3D
Middlebury
6.0
DISTS
D1655
40099
01003
(18011
30
15
0.55
1230
0.83
1.000 0309
0350
Baseline (meter)
Baseline (meber)
Baselime (moter)
Net-
TanisAnddeemples
WilRGBB
08
D3STS
Bascline
Basellne
SHARP
Gen3C
Crawerrfeer
TMP1
Hlash3
LVSM
(a) Plots of camera baseline size vs, DISTS metric.
Middlebury
0.2
1:2
DINTS
LISTS
6:0
013
4000
31/00
1001
12
15
20
2.5
60
6125
020
0225
02
1275
6.00
Beseline (miter)
Beseline (mster)
Baseline (mittr)
ScmnNet++
TanissAnd Templls
WiLdRGBD
0.10
13575
0100
0000
Saceline (meter)
Baseline (mober)
Baseline (meber)
ViewCrafter
TlashDD
(b) Plots of camera baseline size vs. DISTS metric, with privileged info.
Figure 7: Motion range analysis on the evaluation set. Shade indicates standard deviation. Un- shaded data points indicate single sample in the bin. Bins without samples are skipped, ef. Fig- ure 6) SHARP works consistently the best with camera baseline sizes < 0.5 meters, and maintains comparable results against diffusion-based approaches larger motion ranges. It remains the best or the second best up to meters.
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alleviate the issue; involving diffusion models with richer priors may be alternative solution in the future.
(a) Input
(b) Rendered novel view
(c) Rendered inverse depth
Figure 8: Depth failures in challenging edge cases.
D.8 ADDITIONAL QUALITATIVE RESULTS
Here we provide extensive qualitative results of all approaches on all datasets in Figures 1.5 1,566] both with and without privileged depth information. LVSM, SVC, ViewCrafter, and Gen3C operate a fixed aspect ratio, therefore we pad their output to match the original image resolution. SHARP consistently produces high-fidelity results. Further video results can be found in https://apple. github.io/ml-shi.
E LLM USAGE DECLARATION
We used Claude Sonnet 4.5 to polish the writing (e.g. check grammar issues and find better syn- onyms), to help layout ETTX tables and figures, and build the front-end interface of the interactive video comparison.
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color alpha
depth
+ perceptual
regularizer
Figure 9: The effect of different loss terms.
None
+ learned adjustment
Figure 10: The effect of learned depth adjustment.
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Stage
Stage 1+-
Figure 11: The effect of SSFT.
Freeze
Unfreeze
Figure 12: The effect of unfreezing the monodepth backbone.
x downsampled
2x downsampled
Full model
Figure 13: The effect of the number of output Gaussians.
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SHARP (ours)
Gen3C
View ViewCrafter Crafter
Flash3D
TMPI
(cm
50cm
100cm
150cm
Figure 14: Extending the range of motion beyond nearby views, with monodepth as privileged depth information. We do show LVSM and SVC as they cannot make use of privileged depth information, see discussions in Section D.4] ViewCrafter and Gen3C operate at a fixed aspect ratio, therefore we pad their output to match the original image resolution.
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Flash33
TMP1
LVSN
SVC
ViewCruftes
Gen3C
SHARP (ours)
Ground truth
Figure 15: Qualitative comparison on Middlebury.
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Flash33
TMPI
LVSM
SVC
ViewCrute Crufter
Gen3C
SHARP (ours)
Ground truth
Figure 16: Qualitative comparison on Middlebury with privileged depth information.
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Flash3D
TMP
LVSM
SVC
View View Crafter
Gen3C
SHARP (ours)
Ground truth
Figure 17: Qualitative comparison on Booster.
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Flash3D
TMP
LVSM
SVC
View Crafter
Gen3C
SHARP (ours)
Ground truth
Figure 18: Qualitative comparison on Booster with privileged depth information.
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Flush3D
TMP1
LVSM
SVC
ViewCrafter
Gen3C
SHARP (ours)
Ground truth
Figure 19: Qualitative comparison on Net++.
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Flush3D
TMP1
LVSM
SVC
ViewCrafter
Gen3C
SHARP (ours)
Ground truth
Figure 20: Qualitative comparison on ScanNet++ with privileged depth information.
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FlashaD
TMPI
SVC
Crawerr
Gen
SHARP (ours)
Ground truth
Figure 21: Qualitative comparison WildRGBD.
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Flash3D
TMPI
SVC
ViewCrafic
Gen
SHARP (ours)
Ground truth
Figure 22: Qualitative comparison WildRGBD with privileged depth information.
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Flash3D
TMPI
IVSM
SVC
ViewCrafter
Gen33C
SHARP (ours)
Ground truth
Figure 23: Qualitative comparison Tanks and Temples.
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Flash3D
TMP1
IVSM
SVC
View Crafter
Gen3C
SHARP (ours)
Ground truth
Figure 24: Qualitative comparison Tanks and Temples with privileged depth information.
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Flush3D
TMPI
LVSM
SVC
CiewCrafter
Gen3C
SHARP (ours)
Ground truth
Figure 25: Qualitative comparison on ETH3D.
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Flush3D
TMPI
LVSM
SVC
ViewCrafter
Gen3C
SHARP (ours)
Ground truth
Figure 26: Qualitative comparison on ETH3D with privileged depth information.
39


Reviews:
[{"review_id": "gMGdeKlHVv", "reviewer": "Reviewer_p7mt", "summary": "This paper focuses on the task of predicting Gaussian primitives from a given image for novel view synthesis from neighbor viewpoints. The key insight lies in a depth guided framework that predicts Gaussian attribute refinement to a initialized 3DGS. The results outperforms current video-based generative models.", "questions": "Please refer to the weaknesses above.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The task is a promising way for VR/AR applications. This paper focuses on a cutting-edge field.\n2. The results are convincing which perform many video based methods that require costly inference.", "weaknesses": "1. The authors should provide a video in the supplementary for a more clear comparison with SOTA methods. Since the method outputs a 3DGS, it is more convincing to attach a video showing novel view synthesis results of the 3DGS.\n2. How large offset range can the model handle? For the regions that are not visible in the current image, does the model has the capability to generatively infer the occlusions and scene extensions? \n3. More recent works like See3D should be compared.\n\nMy most concern lies in a lack of direct video showing the quality of GS rendering.", "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": "XoCbsHX0TY", "reviewer": "Reviewer_rSXy", "summary": "This paper introduces SHARP, a method for photorealistic view synthesis from a single image. It predicts a 3D Gaussian representation of the scene in under one second using a single feedforward neural pass. The resulting representation allows real-time, high-resolution rendering of nearby viewpoints with accurate metric scaling. SHARP achieves state-of-the-art image fidelity, outperforming diffusion-based and feedforward baselines.", "questions": "Please refer to the weakness section.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The proposed method is fast and efficient, while achieving high-quality results. \n\n2. The experimental results demonstrate strong performance across multiple datasets and metrics. \n\n3. The writing is clear and the engineering contributions are solid.", "weaknesses": "1. The work is more like a system engineering paper rather than a novel research contribution. The scientific novelty is limited. The authors should better highlight the key innovations. \n\n2. It's better that the authors can provide video results to showcase the real-time rendering capabilities. \n\n3. The font used in the paper seems to be non-standard.", "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": "rea7dWlwsq", "reviewer": "Reviewer_LvQ8", "summary": "The paper introduces SHARP, a feed-forward method for real-time view synthesis from a single image.\nIt predicts a 3D Gaussian representation via one forward pass (<1 s on an A100), enabling photorealistic nearby view rendering at over 100 FPS.\nSHARP combines a Depth Pro backbone, a learned depth-adjustment module, and a Gaussian decoder trained end-to-end.\nExperiments show strong improvements (25–40% LPIPS/DISTS gains) over prior feed-forward and diffusion-based methods. \nHowever the visualization only shows 1-to-1 view synthesis.", "questions": "Please see the weakness.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Novel combination of monocular depth inference and 3D Gaussian Splatting with impressive speed and fidelity.\n\nClear architecture and training pipeline; loss design and curriculum are well justified.\n\nExtensive comparisons across datasets and perceptual metrics.", "weaknesses": "- Novel-view range unclear. \n\nThe paper does not specify how far target views are from the input. Report actual displacement (e.g., angle, translation) and analyze performance versus view distance.\n\n- View-to-view consistency. \n\nSince only one novel view for each scene is reported, temporal stability across continuous camera motion is unknown. Evaluating flickering with continuous multiple novel view renderings for frame-to-frame consistency is desired.\n\n- Multi-view generalization. \n\nCan SHARP handle multi-view-to-multi-view synthesis, or is it strictly single-image input due to the monocular depth backbone? Clarify applicability to general NVS pipelines. As most baselines are designed for multiple to multiple NVS, it is a bit unfair setting to directly compare.\n\n- 3DGS necessity. \n\nThe model predicts ~1.2 M Gaussians even for small parallax. This appears over-parameterized; justify why a full 3DGS is required. A comparison with pure pixel output like LVSM  under the same training setup would clarify whether 3DGS truly improves quality. And whether 3DGS really ensures 3D consistency under this setting is unclear.\n\n- Reproducibility. \n\nSynthetic data generation and rendering details are not publicly available.", "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": "yRh0fBZmQw", "reviewer": "Reviewer_PNr1", "summary": "The paper introduces SHARP (Single-image High-Accuracy Real-time Parallax), a method for photorealistic view synthesis from a single image. SHARP directly regresses a 3D Gaussian representation through a single feedforward pass, achieving real-time high-resolution rendering with metric scale. It combines a Depth Pro encoder, a modified DPT depth decoder, a U-Net-based depth adjustment module, and a Gaussian decoder, trained in two stages—synthetic pretraining and self-supervised finetuning. Experiments on multiple datasets demonstrate strong zero-shot generalization and significant improvements over prior methods in both quality and efficiency. The main contributions include efficient single-image 3D Gaussian regression, depth ambiguity resolution, real-time rendering capability for AR/VR, and establishing a new benchmark for monocular view synthesis.", "questions": "-1. The paper notes that SHARP is designed for “nearby views.” However, since the term “nearby” can be subjective and application-dependent, could the authors clarify the model’s effective operational range? For instance, the authors could consider plotting key perceptual metrics (e.g., DISTS or LPIPS) against the camera’s baseline distance or angular deviation from the source view, or providing a similar form of analysis to illustrate the model’s effective operational range.\n\n-2. Have the authors encountered cases where the Depth Pro still produces inaccurate estimates even after the depth adjustment module? If so, how does such inaccuracy affect the quality of the generated results, and are the reconstructions still satisfactory under these conditions?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Originality: The primary novel contribution is the formulation of a single, feed-forward network that can directly regress the parameters of a complete, high-resolution 3D Gaussian representation from a single image. This moves beyond prior works that might generate simpler representations or require slow, per-scene optimization. Furthermore, the introduction of a learned depth adjustment module, inspired by Conditional Variational Autoencoders (C-VAEs), is a clever adaptation to handle the inherent ambiguity in monocular depth estimation, optimizing the depth map specifically for the end-goal of high-fidelity view synthesis rather than just metric accuracy. This demonstrates a nuanced understanding of the problem's core challenges.\n\nQuality: The work is technically solid and experimentally comprehensive. Technically, it employs a well-structured architecture that includes a Depth Pro monodepth backbone (with selective unfreezing for task adaptation), a custom Gaussian decoder, and a detailed loss formulation combining color, perceptual (LPIPS), and alpha losses with regularizers for floaters, smoothness, and Gaussian variance to mitigate common 3D reconstruction artifacts. Experimentally, SHARP is evaluated on several datasets (Middlebury, ScanNet++, WildRGBD, etc.) and compared with recent state-of-the-art approaches, including diffusion-based methods. The ablation studies systematically examine key components—such as perceptual loss, depth adjustment, self-supervised fine-tuning, and backbone unfreezing—and the use of perceptual metrics (LPIPS, DISTS) instead of traditional ones (PSNR, SSIM) is appropriately justified.\n\nClarity: The paper is clearly written and well-structured. The introduction defines the goals—fast synthesis, real-time rendering, and metric scale—while figures effectively illustrate key achievements, such as reduced latency and high-fidelity outputs. The methodology is logically organized, with clear system diagrams and concise mathematical formulations.\n\nSignificance: SHARP addresses the challenge of synthesis latency, reducing generation time from minutes to under a second and making real-time single-image view synthesis more practical for interactive applications such as AR/VR. It also sets a strong baseline for feed-forward methods, demonstrating that a carefully designed regression-based approach can achieve high-fidelity nearby views efficiently. These results may guide future research on combining quality and speed in view synthesis.", "weaknesses": "-1. In the ablation study for SSFT (Table 11), the quantitative metrics for the model with and without SSFT are very close, with some metrics even slightly worse after fine-tuning.\n\n-2. The model's Stage 1 training relies on a large-scale, in-house synthetic dataset. While the quality of this dataset is likely a key contributor to the model's success, it poses a challenge for reproducibility and fair comparison.", "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)