Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation
Authors: Sherwin Bahmani
Tianchang Shen
Jiawei Ren
Jiahui Huang
Yifeng Jiang
Haithem Turki
Andrea Tagliasacchi
David B. Lindell
Zan Gojcic
Sanja Fidler
Huan Ling
Jun Gao
Xuanchi Ren
Venue: ICLR 2026 Poster (2026)
Keywords: 3d
video diffusion
gaussian splatting
Primary area: generative models
TL;DR: Feed-forward 3D and 4D with camera-controlled video diffusion models

Abstract: The ability to generate virtual environments is crucial for applications ranging from gaming to physical AI domains such as robotics, autonomous driving, and industrial AI.
Current learning-based 3D reconstruction methods rely on the availability of captured real-world multi-view data, which is not always readily available. 
Recent advancements in video diffusion models have shown remarkable imagination capabilities, yet their 2D nature prevents their use in simulations where a robot needs to navigate and interact with the environment.
In this paper, we propose a self-distillation framework that aims to distill the implicit 3D knowledge in the video diffusion models into an explicit 3D Gaussian Splatting (3DGS) representation, eliminating the need for multi-view training data.
Specifically, we augment the typical RGB decoder with a 3DGS decoder, which is supervised by the output of the RGB decoder.
In this approach, the 3DGS decoder can be purely trained with synthetic data generated by video diffusion models.
At inference time, our model can synthesize 3D scenes from either a text prompt or a single image for real-time rendering.
Our framework further extends to dynamic 3D scene generation from a monocular input video.
Experimental results show that our framework achieves state-of-the-art performance in static and dynamic 3D scene generation. Video
results: https://research.nvidia.com/labs/toronto-ai/lyra

Published as a conference paper at ICLR 2026
LYRA: GENERATIVE 3D SCENE RECONSTRUCTION VIA VIDEO DIFFUSION MODEL SELF-DISTILLATION
Sherwin Bahmani 1,2,3 1.2.3 Tianchang Shen 1,3 Jiawei Ren' Jiahui Huang' Yifeng Jiang'
Haithem Turki!
Andrea Tagliasacchi David B. Lindell Zan Gojeic'
Sanja Fidler Huan Ling' Jun Gao Xuanchi Ren 1.2.3+
NNVIDI University of Toronto Veccoor Institute "Simon Fraser University
*equal contribution
https::/research.nt nvidia. com//aabs/toroattoaa///
Input Image
Rendered Depth
3D Gaussian Splatting Renderings
Input Video
Rendered Depth
Dynamic 3D Gaussian Splatting Renderings
Figure 1: Feed-Forward 3D and 4D Scene Generation. From a single image (top), Lya infers a 3D Gaussian Splatting (3DGS) representation in a foed-forward fashion, through self-disilling video diffusion model without requiring real-world multi-view data. With video input (bottom), Lyra infers dynamic 3DGS that offers interactive control in both time (rows) and viewpoint (columns).
ABSTRACT
The ability to generate virtual environments is crucial for applications ranging from gaming physical AI domains such roboties, autonomous driving, and industrial AI. Current learning-bass based 3D reconstruction methods rely on the availability of captured real-world multi-vier data, which is not always readily available. Recent advancements in video diffusion models have shown remarkable imagination capabilities, yet their 2D nature prevents their use in simulations where a robot needs to navigate and interact with the environment. In this paper, we propose self- distillation framework that aims to distill the implicit 3D knowledge the video diffusion models into explicit 3D Gaussian Splatting (3DGS) representation, eliminating the need for multi-view training data. Specifically, we augment the typical RGB decoder with 3DGS decoder, which is supervised by the output of the RGB decoder. In this approach, the 3DGS decoder can be purely trained with synthetic data generated by video diffusion models. At inference time, our model

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can synthesize 3D scenes from either a text prompt single image for real-time rendering. Our framework further extends to dynamic 3D scene generation from a monocular input video. Experimental results show that our framework achieves state-of-the-art performance in static and dynamic 3D scene generation. Video results:
nvidia com www.//aa////..../.a http:////b/ooooooaaaaaaaaaa
INTRODUCTION
Creating high-quality 3D environments at scale is long-standing challenge in computer vision and graphics, empowering applications across film-making, VR/AR, and physical Al with closed-loop simulation. These applications require explicit 3D representations that support real-time rendering. physical interaction, and consistent multi-view synthesis.
Recent advances in neural 3D reconstruction (Mildenhall al. 2020 Kerbl et al. 2023 Moenne Loccoz et al. 2024) enable recovering such representations from posed images. However, the reliance on accurate camera poses and high-quality images significantly limits their scalability. The challenge is even greater for dynamic scenes, which typically require synchronized multi-camera setups (Li al., 2022). Reconstruction methods are also inherently constrained the observed content and cannot extrapolate beyond the input views. Recent works aim to mitigate these limitations by using feed-forward reconstruction models (Zhang et al., 2024e) Ren et al. 2024c), but these approaches face their own bottleneck: the scarcity of diverse, large-scale 3D training data, which leads to poor out-of-domain generalization.
Wan 2025) offer a promising alternative. Trained on mas- sive internet-scale video corpora, they achieve impressive fidelity and generalization across diverse environments. By learning from real-world videos with varied camera trajectories, ranging from handheld motion to cinematic panning and drone footage, these models implicitly encode cues about the underlying 3D world without requiring multi-view training data. Unlike reconstruction approaches (Charatan et al. 2024), generative models can also hallucinate plausible content beyond what is visible in the input frames. Yet, video diffusion models generate only 2D frames, lacking explicit 3D representations. This limits their use in simulation and downstream tasks that demand geo- metric consistency, long-term coherence, and physical interaction. Encouragingly. recent work (Wang) et al. 2024e Ren et al. 2025) has shown that video models can be adapted for explicit camera control using relatively small-scale posed datasets. This ability to generate posed image sequences transforms them into a powerful tool that can serve both input and supervision for 3D reconstruction models.
Video diffusion models (Cosmos 2025
Motivated by this insight, we bridge these two paradigms, reconstruction and generation, through what we call generative 3D scene reconstruction. We introduce Lyra, novel method for generating explicit 3D environments from the latent representations of video diffusion models in a single forward pass. Cruically, Lyra is trained in a self- celf-distillation framework (Fig. 2). with 3D Gaussian Splatting (3DGS) decoder operating directly in the latent space a video diffusion model. Given a single input image video, we sample camera trajectory conditioning input, denoise the resulting video latent, and decode it along two parallel branches. The first branch uses the standard RGB decoder to synthesize video sequence, while the second is our 3DGS decoder, which produces an explicit 3D representation. Together, these branches form a self-distillation framework in which the RGB branch (teacher) supervises the 3DGS branch (student). To extend viewpoint coverage, we sample multiple camera trajectories, generate multiple video latents, and train the 3DGS decoder to fuse information across them while mitigating long-term and multi-view inconsistencies.
This generative reconstruction framework provides three key benefits: (i) it enables generation of sarge-scale synthetic environments spanning diverse scenarios directly from video diffusion models, removing the need for real-world multi-view captures; (ii) by operating in latent space of video model, it allows efficient processing of multiple views without the heavy memory overhead pixel- space feed-forward reconstruction methods; and (iii) its explicit 3DGS output guarantees geometric consistency, providing representations that are directly applicable to downstream tasks such as physical simulation. Consequently, inference time, Lyra generates high-quality 3D Gaussian scenes from monocular input that support real- time rendering, without requiring any additional optinization or post-processing.
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Previous Work
Self-Distillation (Ours)
Multi- View Datasets
Loss
Images
Images
Images
Render
Loss
3D Cansssin
Decoder (RGB)
Decoder (3DGS)
Images
Render
Vidco Diffusion Model
Gaussians
Multi- View Reconstruction Blocks
Encoder (RGB)
Images
Cameras
Images
Figure 2: Self-distillation framework of Lyra. Previous work (left) (Zzymanowicz al. 2025b
trains multi-view reconstruction blocks using real-world datasets with limited diversity (Zhou et al. 2018: Ling al. 2024b). In contrast, we propose self-distillation framework (right) for generative 3D scene reconstruction. Precisely, a pre-trained namera-controlles video diffusion model with its RGB decoder output (teacher) supervises the rendering of the 3DGS decoder (student). Using a video model pre-trained on diverse 2D video data allows us to provide diverse multi-view supervision.
We further extend this self- elf-distillation framework to dynamic 4D generation from monocular video. In this setting, the video model (teacher) provides space time supervision, while the student learns to produce time-conditioned 3DGS representations that enable novel-view synthesis of dynamic scenes.
Overall, our work makes the following contributions:
We introduce self-distillation framework that trains a 3DGS student decoder using pre-trained camera-controlled video diffusion model as the teacher, eliminating the need for captured multi-view real-world data.
Our framework extends to dynamic scenes to support 4D reconstruction from a monocular video.
Our model generalizes across diverse scenes, achieving state-of of-the-ar art results in single- image 3D scene generation and single-video 4D scene generation.
2 RELATED WORK
Multi-view image generation. Early works on multi-view image generation (Wasson al., 2023
Liu et al. 22033 Shi et al. 2024 Wang & Shi (223)) mainly focus on object-centric scenes without background. Follow-up works (Sargent et al. 2024, Tang al.) 2023b Schneider al. 2025)
extend multi-view image generators to the scene scale. CAT3D [Gao et al. (024b) extends a pre- trained image diffusion model for multi-view image generation from any generated or real image input. In a subsequent stage, the generated multi-view images are reconstructed into an explicit 3D representation, e.g., NeRF (Mildenhall et al. 2020) or 3DGS (Kerbl et al., 2023). Moreover, these methods have been extended to generate dynamic 3D scenes from multiple viewpoints using space-time diffusion model (Watson et al. 2024 Wu et al. 20255 Kuang et al. 2024 Wang et al.) 2025a Even though these methods demonstrate impressive results, grounding the generations in an explicit 3D representation requires an expensive optimization stage that not amortized across scenes. Instead of creating multi-view images and then reconstructing using optimization, we are interested in directly generating a 3D scene from text or a single image.
Camera-conditioned video generation. There has been significant progress in fine-tuning video diffusion models for 3D camera control. MotionCtrl (Wang et al.) 2024c) pioneered camera control by conditioning pre-trained video models with camera poses. Follow-up works (He et al.) 2024a] Xu et al. 2024c| Bahmani et al. 2025a b) represent cameras as Plücker coordinates for pixel mexel-wiss wise conditioning. Another line of work (Hu et al.) 2024 Hou al. 2024 Zhang et al., (024b) investigates training-free camera control of video diffusion models. Recently, SynCam Castmerrtrr (Bai et al.) 2025b) and ReSteMaster (Bai et al., 2025a) demonstrated impressive camera-controllles video synthesis using large-scale synthetic training data. While these works achieve compelling results, their output is not grounded in 3D. Our work is orthogonal to this line of work, as we tackle the task of -orward 3D generation from camera-controlles video models, allowing novel viewpoint rendering after generation. Concretely, we build upon GEN3C (Ren et al. 2025), a recent camera-con cumera-controlled video diffusion model, and use it as teacher within self-distillation aatiin framework for 3D reconstruction.
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Feed-forward 3D models. Early work feed-forward 3D reconstruction mainly focused on object- centric scenes without background. These methods directly predict a NeRF (Hong et al. 2024 Li
et al. 2024b) or 3DGS (Tang et al.
2024a| Szymanowicz et al., 2024) from either text an image.
More recent works (Charatan et al. 2024 Zhang et al. 2024e] Szymanowicz et al. 2025a Ren et al.
2024c) focus on scene-level 3D reconstruction, typically regressing per-pixel 3DD Gaussians from
one or more input images. However, most scene-level 3D reconstruction methods are limited to training distribution scenes, e.g., RealEstate (Zhou et al. 2018), with limited generalizability to generated scenes. Recent methods extend feed-forward 3D reconstruction models to generate scenes. Bolt3D (Szymanowicz al., (025b) trains a pointmap (Wang al. 2024b) autoencoder generate pointmaps along the multi-view images, used for feed-forward 3D reconstruction. Closely related to our work, Wonderland (Liang et al. 2025a) uses a camera-controllees video model to generate 3D Gaussians with feed-forward network. In contrast to these works, we use camera-controlled video model as a teacher within a self-distillation framework to train our student 3D model without requiring real-world multi-view data. Importantly we show a simple extension of our framework for ferwafo 4D scene generation, an unexplored task to date.
3 SELF-DISTILLATIO USING VIDEO DIFFUSION MODELS
Our key idea to distill the implicit 3D knowledge embedded in video diffusion models into an explicit 3DGS decoder capable of producing high-quality 3D representations. To this end, we build a teacher-student framework in which the video diffusion model (teacher) generates RGB videos that supervise the 3DGS decoder (student) that operates in the same latent space, illustrated in Fig. 2 In this section, we detail our video diffusion model backbone (Sec.[.1] and its role in self-distillation
for the 3DGS decoder (Sec. 3.2) We discuss the design choice for the 3DGS decoder in Sec. and
and
outline minimal changes required to adapt the pipeline to dynamic 3D scenes in Sec. 5
3.1 BACKGROUND: CAMERA-CONTROLAN AND SD-CONSISTENT VIDEO DIFFUSION
Since we supervise 3DGS decoder only with the camera-controlled video diffusion model, heavily rely on its 3D consistency. Due to this, we build our approach on GEN3C (Ren et al. [2025). a recent conditiond video diffusion model. In the following we provide an overview of the key design choices made in GEN3C.
Spatiotemporal 3D cache. To improve video consistency and camera control precision, GEN3C constructs a spatiotemporal 3D cache {P*.'} from input image(s) or videos, where each pf." is a colored point cloud obtained by unprojecting the depth estimation (Wang et al., (0224) of an RGB image captured from camera viewpoint u at time f. The cache is organized as an L x array, where I, denotes the number of frames (temporal length) and denotes the number of camera views.
Rendering and structured guidance. To leverage this cache, GEN3C renders each point cloud from arbitrary given camera poses, producing RGB images I and disocclusion masks M via (IS.,, MM.) R(P C²), where R denotes the rendering function that projects the 3D point cloud pt. onto the 2D camera plane according to the camera pose C' at time Given a sequence of camera poses C = (C) ..... ......C.),, rendering all cache elements produces V videos of length L, which can be stacked into image sequences I" E X.33 H X and mask sequences M E IR f fx for each view v. The disocclusion masks indicate areas that the video diffusion model should fill in. These renderings serve as structured visual guidance for subsequent video generation.
Video variational autoencoder (VAE). In diffusion- based video generation models, video varia- tional autoencoder (VAE) (Kingma & Welling. 2013) Rombach et al.) (022) is commonly employed to compress videos into a lower-dimensional latent space for efficient training and inference. Given RGB video I E X a pre-trained VAE encoder & will encode the video into a latent space, i... z = E(I E _ xCxxxxx The training and inference of the diffusion model are then performed in this latent space. The final video i = Drgs (z) is decoded with pre-trained VAE decoder D, Dryb. In this paper, we adopt the pre-trained GEN3C [Ren et al.) 2025| Cosmos 2025) model. Specifically, 1)/7 + 1, and the spatial dimensions are = H/o and w = W/o, where the temporal compression factor is 7 = 8 and the spatial compression factor is 8.
the latent channel dimension is C 16, the temporal dimension is L'
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Training
Decoder (3DGS)
Loss
Gaussians
Transposed 3D Conv
Render
Imsgoe
Clusn
Multi- View Reconstruction Blocks
Sum
Decoder (RGB)
Decoder (3DGS)
Dynamic
Vidco Diffusion Transformer
Patch
Patch
Patch
Encoder (RGB)
Patch
Encoder
Encoder
Encuder
Video Latents
Plucker Eimb.
Target
Muliple Trarectriess
Figure 4: 3D Generative Reconstruction Framework. Our pipeline builds upon camera-controlle video diffusion model (Ren et al. 2025) pre-trained on large scale data. We train a 3D Gaussian Splatting (3DGS) decoder by aligning the 2D image renderings of generated 3DGS scenes with the RGB-decoded generations of the pre- pre-trained video model. We only train the 3DGS decoder while freezing the pre-trained autoencoder and diffusion model. At inference time we directly use the 3DGS decoder, without requiring the RGB decoder anymore. Time conditioning within the 3DGS decoder allows us easily extend our approach from static to dynamic 3D scene generation.
3.2 SELF-DISTILLATION
We train the 3DGS decoder D, as student under a strchet-student paradigm, where camera- controlled video diffusion model V serves the teacher. For diverse supervision, we curate large-scale set of diverse text prompts with large language models (OpenAl generate images I with image diffusion model (Black Forest Labs] 2024), and expand into multi-view sequences using GEN3C (Ren et al. 2025).
2025 Bai et al. 2023),
Teacher-student setup. Given an input image / and sampled camera trajectory {C*) |t= the video diffusion model V generates a denoised video latent z = V(I, (C*). The latent z is decoded along two branches: the pre-trained RGB decoder Dryb produces video frames Ip = Drgb(z), while the 3DGS decoder D. outputs explicit 3D Gaussians G. Rendered views from 3DGS (student) defined ID, Render( G, (C') }/=1) and are supervised to match RGB frames ID, (teacher). forming the self-distillation loop.
Multi-trajectory supervision. In paper, enlarge viewpoint coverage for the input image, we sample V = 6 camera trajectories per input image, as shown in Fig. For each trajectory v. we construct a spatiotemporal cache {P*.') //n along with L = 121 camera poses {C*.)/ It Passing these caches through video diffusion model V yields latents z". which the teacher decodes into RGB frames I (z") The 3DGS decoder
Zooni
Left
Stari
D, learns to fuse these multiple z" into coherent Gaussians G. while filling disoccluded regions. Through this self-distillation design, the 3DGS decoder D, is trained entirely from synthetic supervision provided by V. Operating in latent space enables efficient aggregation of multiple trajectories, while the explicit Gaussian output G enforces geometric consistency and supports downstream simulation and real-time rendering.
Zaom-oa
Right
Figure 3: Sampled six camera trajec- tories to maximize view coverage.
4 FEED-FORWARD RECONSTRUCTION FROM MULTI- MULTI-VIEW VIDEO LATENTS
Our feed-forward 3DGS decoder D3 is designed to transform the synthesized multi-vien latents generated by video model V into explicit 3D representation that can be rendered from arbitrary viewpoints. We choose 3D Gaussian Splatting (3DGS) (Kerbl et al. 2023) due to its explicit representation and fast rendering speed. We visualize our detailed 3DGS decoder in Fig. (right).
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4.1
3DGS DECODER
Scaling Latent-Based 3D Reconstruction. Previous feed-forward reconstruction frameworks generate 3D Gaussians for each pixel, and thus, are limited by the number and resolution of input views they can handle. For example, GS-LRM (Zhang et al.] 2024e) operates on 2-4 images at 512 512 resolution, while AnySplat (Jiang et 2025a) is trained with 24 images at 448 448 resolution. Our video model synthesizes x L 6 x 121 = 726 input views at 704 x 1280 resolution- far beyond the capacity of existing methods, which exceeds GPU memory limit. The bottleneck lies in the attention mechanism applied to visual tokens, whose memory and compute requirements grow with the number of pixels. To overcome this limitation, we avoid scaling in pixel space and instead operate directly in the compressed video latent space produced by our camera- controlled video diffusion model. The nult video latents are denoted as Z R RVX X7xx''xCC XCxhx
Architecture. The 3DGS decoder D, first maps its inputs into the hidden dimension of the main reconstruction blocks and outputs per-pixel 3D Gaussian features G E R X WW x14, Specifically, the inputs are the multi-view video latents Z and encoded Plücker embeddings E, both with latent dimensions Z, E E RV VVLL' xCxxxu Each input component is first patchified to match the hidden dimension of the main reconstruction blocks. We do not require any additional visual encoder-only spatial 2 x 2 patchification layer (Dosovitskiy et al. (021] to transform the video latents into flattened tokens for the reconstruction network. The sum of both inputs is processed through the reconstruction
blocks. We follow the design introduced in Long-LRM Ziwen et al. 2024), i.c., one block consists of one Transformer (Vaswani al. 2017) layer followed by seven Mamba-2 (Dao & Gu (024) layers. We repeat the block twice to get 16 layers with 512 hidden dimensions. Finally, a transposed 3D convolution maps the hidden representation to Gaussian channels: 3D position (zc, y, z), scale 8y,88) (8,,8y, 8z), rotation quaternion (4e: yz: 4y Uz), opacity and RGB (r,g, b). Formally, the static decoder expressed as G =D (Z, E).
Plücker embeddings. Raw Plücker embeddings are first computed from camera poses {C*.) } and intrinsics as E V
We reuse the RGB encoder & from the pre-trained VAE to encode Plücker embeddings. Specifically, and the 3-dimensional cross product of ray directions and origins using E, and concatenate latent along the channel dimension to obtain Esne E R.V L x2Cxhxw A lightweight MLP maps E to E E IR x xCxhxw reducing the channel dimension by factor of 2 to match video latent Z.
separately encode the 3-dimensional ray directions
4.2 LOSS FUNCTION
Image-based supervision. We supervise our 3DGS decoder with an image-based reconstruction loss. The reconstruction loss is split into Mean Squared Error (MSE) loss Lmse and an LPIPS loss Lipips (Zhang et al., 2018 using VGG (Simonyan & Zisserman 2014) as a feature extractor.
Depth supervision. We observed that the decoder trained with only RGB loss often produces flattened geometry. To address this, we additionally supervise the rendered depth maps using consistent video depth estimated by an off-the-shelf system ViPE (Huang al. 2025), and the scalc-invariant depth loss Cdepth from Long-LRM (Ziwen et al. 2024
Opacity- -based pruning, Similar to Long-LRM (Ziwen al. 2024), we use an LI regularization on the opacity Lopacity and remove the Gaussians with the lowest 80% opacity.
Total loss. Our total loss is computed as the weighted sum of all losses with weight factors X
Amec Lyne + Aipipe Lipipe + Adepth Lddpth + Aopacity Copaesty
where we set Amse 1.0, Atpips 0.5, Adepth = 0.05, and Nopacity 0.1.
5 EXTENSION TO DYNAMIC 3D SCENES
Our approach can be extended handle dynamic 3D scenes with minimal changes, outlined below.
Self-distillation for dynamic scenes. Our dynamic 3D setup closely follows the design of the static 3D counterpart. Instead of single image, the video model takes single video of length with corresponding camera poses as input and generates multi-view video latents capturing the same underlying motion. For video inputs, we follow protocol similar to the static case: sampling

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Viewpoints
w/o data augmentation
Time
Ours
Time
Figure 5: Dynamic data augmentation. When naively supervising the time-aware 3DGS decoder, observe artifacts in the generated 3D Gaussians. Specifically, early timesteps from extreme poses exhibit low opacity in regions not covered by the supervision signal. To address this, we augment the supervision data with motion-reversed video, ensuring that each timestep is observed from the full spatial coverage, thereby preventing low opacity artifacts in the early timesteps.
diverse text prompts with large language models, then generating videos using video diffusion models (Cosmos 2025 Wan (025). The generated videos are then annotated with camera poses and depth maps using VIPE (Huang et al. 2025).
Dynamic 3DGS decoder architecture. We follow the bullet-time design of Liang et al. (025b) and design our dynamic 3DGS decoder D, to generate dependepnddntt 3D Gaussians for all the video frames. Specifically, the dynamic 3DGS decoder D, closely follows the architecture of D., except that we augment the D. input with the encoded source and target time embeddings The Tist IR XVL'''x Tre.aw E V xW and the target time is Tig.taw E RRVx XXVXxxxXXX Both are spatially repeated and augmented with a 2-dimensional sinusoidal embedding to expand the channel dimension to 3, then encoded with the RGB encoder & to latent dimensions THc T E V xCxhxw The target time is repeated along the latent temporal dimension match T'" During training, target time step is randomly sampled, and the corresponding 3D Gaussians are decoded and supervised across all trajectories generated with dynamic data augmentation. We fine-tune D2 from pre-trained D, with the patchification layers of The and T initialized to zeros. Twee and Ts are directly added to the sum of Z and E. Formally, the dynamic decoder is defined as G Dz E. T T
The unencoded (raw) source times are assigned to each input frame, forming H
Dynamic data augmentation. In the static 3D scenes, where time is not a factor, frames from different timesteps can all be used to supervise the 3DGS renderings. In contrast, for dynamic 3D scenes, the 3DGS changes over time, so only frames from the corresponding timestamp are valid for supervision. This restriction can lead to a trivial solution, where the 3DGS at a given timestep simply ignores information from frames other timesteps (see Fig. 5).
To mitigate this issue, we introduce dynamic data augmentation strategy that balances supervision across timesteps. Early frames are naturally associated with viewpoints close to the input image, whereas later frames correspond to more distant viewpoints. To counter this imbalance, we augment the training data with paired supervision views for each timestep we reverse the input video in frame order and feed it into the video model V. which produces six additional multi-view sequences. After reversing these sequences back to the original motion order, we obtain six trajectories that progress inward (from far viewpoints toward the input). Combined with the original six outward trajectories, this yields 12 supervision views per timestep. Importantly, this augmentation is applied only during training: the reversed trajectories act purely as extra supervision signals prevent collapse from pruning artifacts and are not required at inference time.
near and one far. Concretely,
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Table 1: State-of-the-art compartsons. We compare our method with previous works for single image to 3D generation using RealEstate DL3DV, and Tanks-and datasets.
RealEstate
DL3DV
Tanks-and aemeles
Method
PSNR t
SSIM
LPIPS 4
PSNR t
SSIM
LPIPS 4
PSNR
SSIM
LPIPS
ZeroNVS
13.01
0.378
0.448
13.35
0.339
0.465
12.94
0.325
0,470
ViewCrafter
16.84
0.514
0.341
15.53
0.525
0.352
14.93
0.483
0.384
Wonderland
17.15
0,550
0.292
16.64
0,574
0.325
15.90
0,510
0.344
Bolt3D
21.54
0.747
0.234
Ours
21.79
0.752
0.219
20.09
0.583
0.313
19.24
0.570
0.336
Figure 6: Image-to-3DGS Generation. We visualize five views from generated 3DGS scenes.
EXPERIMENTS
6.1 EXPERIMENTAL SETUP
Datasets. To train our model, do not any existing multi-view datasets; instead, construct our own, which we call the Lyra dataset, relying solely on our video model to supervise the 3DGS decoder. The 3D reconstruction setup uses 59,031 images, while the 4D setup has 7,378 videos. All data are from diverse text prompts, spanning scenarios such as indoor and outdoor environments, humans, animals, and both realistic and imaginative content. We synthesize six camera trajectories for each image (3D) or video (4D), yielding 354, 186 videos for 3D and 44,268 videos for 4D.
Baselines. We compare our method with the state-of-the-arr approaches for generative single- image-to-3D reconstruction, i.c., ZeroNVS (Sargent et al., 2024). ViewCrafter (Yu et al. 2024b), Wonderland (Liang et al.) 2025a), and Bolt3D (Szymanowicz al. 2025b). Note that no source code is available to evaluate these methods on our out out-of-distribution evaluation set, hence we mainly rely on reported quantitative comparisons from the papers.
Evaluation. We follow previous works to evaluate our model on the task of single-image to 3D using RealFstate 10K (Zhou et al.) 2018), DL.DD (Ling et al. 2024b), and Tanks and Temples (Knapitsch) al. 2017). We follow the evaluation protocol outlined in Wonderland (Liang et al. (225aa) and Bolt3D (zymanowwicz et al. 20255). We evaluate the performance using standard reconstruction metrics, i.e., PSNR, SSIM, and LPIPS.
6.2 MAIN RESULTS
Quantitative results. We show quantitative evaluations for single image-to-3D in Tab. Our method outperforms previous works on all benchmarks across metrics. Hence, the main improvements to further boost the quality lie in the development of stronger video generative models, reconstructions will directly benefit from them. We provide additional quantitative comparisons on the Lyra dataset in Appendix
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Ours
w/o self-distillation
wo multi-view fusion
w/o LPIPS loss
Figure 7: Ablations. We visualize ablation results by rendering the same extreme novel viewpoint after image-to-3DGS generation; depth visualizations and ablations are provided in Appendix D.4
Qualitative results. We visualize novel view renderings from our generated 3DGS scenes in Fig. and Fig. [ but strongly recommend the reader to our supplementary webpage for video results. Our method generates high-quality novel view content for 3D/4D scenes in unseen regions while maintaining consistency with the input image/video. We visualize qualitative comparisons on the Lyra dataset in Appendix D
6.3 ABLATIONS
In Tab. 2 we motivate our design choices by ablating key components out-of-distribution diverse prompts. We compare 3DGS renderings with videos generated by our camera- camera-controlled video diffusion model.
Real data only. Instead using disfldistillation, we train a model using only real multi-view datasets, i.e, RealEstaaelOKK (Zhou et al. 2018) and DL3DV (Ling al. 2024b). These datasets commonly used in previous works, but lead to limited generalizability to -istr--iitto scenes.
Self-distillation real data. Perhaps surpris- ingly, joint training with self-distillation and real data does not improve over using only self- distillation without real data. This confirms that self-distillation diverse and consistent enough to learn reconstruction model.
Table 2: Ablation study on Lyra dataset.
Method
PSNR
SSIM
LPIPS
Ours
24.77
0.837
0.224
Data
No depth loss. Depth loss prevents flat geome- try and even slightly improves image- based met- rics. We visualize depth maps in Appendix [D.4
real data only
19.08
0.659
0.413
self-distill. + real data
24.74
0.823
0.236
Loss
w/o depth loss
24.31
0.811
0.247
No opacity pruning. We keep all Gaussians instead of pruning them by opacity. Learning to prune most of the 3D Gaussians makes the output representation more compact and slightly improves visual quality. Rendering at Hl 704. and W = 1280 takes 18ms with pruning vs. 30ms without pruming, 1.67x speed up.
w/o opacity pruning
24.55
0.820
0.237
w/o LPIPS loss
23.74
0.766
0.370
Architecture
w/o multi-view fusion
17.73
0.632
0.446
w/o Mamba-2
24.58
0.818
0.241
w/o latent 3DGS
OOM
No LPIPS loss. Adding the LPIPS loss improves the robustness to input inconsistencies and enhances the preservation of high-fie frehurrryyency details.
No multi-view fusion. A naive implementation would be to generate 3D Gaussians for each camera trajectory independently and then fuse the 3D Gaussians into one point cloud. However, observe significant advantages by learning the multi-view fusion from different trajectories with our reconstruction blocks, where each token attends to the others.
No Mamba-2. We replace our joint Transfornee/Mambaa with Transformer-only blocks and observe slightly lower quality. One forward pass with V 6, L. = 121, H 704, and W 1280 takes 32 1ms with joint blocks in comparison to 20922ms with Transformer-only blocks, a 6.5 x speedup.
No latent-based 3DGS. Previous works operating in the pixel space, such as BTimer (Liang et al.) 2025b), are restricted to 12 input frames, while we can take to 726 input frames. Consequently, operating the 3DGS decoder in the pixel space instead of the latent space leads to out-of of-memory.

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Published as a conference paper at ICLR 2026
7 CONCLUSION
In this work, we propose Lyra, a novel 3D and 4D generation framework relying only on a single image video input. Instead of collecting multi-view datasets, introduce camera-controlled video diffusion models as teachers within self-distillation framework for student 3DGS decoder. Our 3DGS decoder operates in the latent space of the video model and directly reconstructs 3D Gaussians without any post-processing or optimization. This design enhances generalizability and coverage across diverse scenes, while our dynamic extension demonstrates the feasibility of 4D generation from monocular video. Currently, the scale and consistency of our generated scenes are bounded by the capacity of our camera-con controlldd video diffusion model. Hence, further improving the video diffusion model will enable large-scale, consistent scene synthesis. Moreover, investigating the adaptation of auto-regressive techniques (Chen et al.] 2024a) into our framework for large- scale generation is an interesting direction for future work. Lastly, modelling motion and tracking information within the reconstruction network, as concurrent work (Lin et al. 2025), could improve visual motion quality.
8 ETHICS STATEMENT
Like all generative AI technologies, there are risks of misuse of our method, including producing misleading synthetic 3D/4D content. While such risks exist, the main goal of this research to contribute to the fields simulation, robotics, and embodied AI by providing scalable and controllable tools for data generation. In this sense, expect most significant contributions to be made through the following capabilities and insights.
The ability to enable realistic simulation for embodied agents: We provide controllable and physically consistent camera motion within generated 3D and 4D environments to support training and evaluating agents that must perceive, navigate, and interact with complex scenes.
Improved scalability for data engines: Our method removes the need for multi-view capture and expensive per-scene optimization, and helps produce high-fidelity. customizable scenes at scale, which is partieularly suitable for research in robotics, reinforcement learning, and closed-loop simulation.
Better technical understanding: As an academic study, this work contributes a foundational case study of how camera control interacts with spatiotemporal generative models, which will benefit research in computer vision, graphics, and embodied AI.
While we caution against deceptive or wrongful use of this technology-wiie -weee misleading and/or unverifiable media could be created with realistic generative outputs safeguards (e.g., provenance tracking, dataset documentation, and good evaluation practices) will be useful in ensuring that generative models advance science and society in positive ways.
believe that the adoption of
REPRODUCIBILITY STATEMENT
To fully reproduce our results and accelerate future research in this area, we release our training and inference code, model weights, and data for both 3D and 4D generation.
10 ACKNOWLEDGEMENTTS
We thank Zian Wang for feedback on the draft.
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A
SUPPLEMENTARY WEBPAGE
We provide video results on our supplementary webpage: httpp:://resrrrc..niidbbb Labs/torootto-ai/yyra
B
ADDITIONAL DETAILS OF VIDEO DIFFUSION MODEL
This section provides additional information on the GEN3C (Ren al. 2025 video diffusion model backbone introduced Sec. 3.1 and how we further improve its 3D consistency.
Conservative mask refinement. A key limitation of GEN3C's forward warping is background leakage, where occluded object parts in the source view (e.g.. lion body in Fig. (8) become visible in novel viewpoints but not properly indicated in the disocclusion mask. This leads to incomplete foreground completion by the video diffusion model, as shown in Fig. (second and fourth columns).
To address this, we adopt conservative strategy: since the geometry of occluded regions in pt, is unknown, we assume all such areas are occupied and derive refined disocclusion masks accordingly. This allows the video model to reason about potentially visible foreground regions that were missed by standard point-based rendering.
Our approach constructs triangular mesh from camera-space points P C'p by connecting spatially adjacent pixels, similar to Hu et al. (202T):
M
U { (Pu.v: Put1.e Pa.041) A (Pu+1.0, Put1p-1: Pu..t11) (u,0)
(2)
where Pu, denotes the 3D point at pixel (u, v) in PA.". This surface mesh serves as boundary between visible and invisible regions. For each rendered pixel, we compare the standard point-based depth D with the mesh-interpolated depth D'M obtained via ray-surface intersection:
0 M (u, v) otherwise orig
D (u, v) < D Di. (u,
Mc v)
where ' denotes the original disocclusion mask from forward warping and is small tolerance factor. When the mesh surface is closer than the original points, we conservatively mask out these regions potential foreground disocclusions. This produces more reliable guidance for video generation, especially under large viewpoint changes, as shown in Fig. g (third and fifth columns).
orig
Input View
Warping
Output
Warping (GENIC)
Warping (GENIC)
Ours
Figure 8: Rendering function comparison. Our model builds upon GEN3C (Ren et al. 2025) that uses forward warped images as camera control conditioning. We visualize forward warping (left) vs. our conservative rendering function (right). The highlighted region is incorrectly filled with background pixels in forward warping but properly masked in ours, enabling correct completion.
C MODEL DETAILS
Progressive training. Directly training the 3DGS decoder 6 trajectories of 121 frames at 704 x 1280 is expensive, hence we train our network progressively, as shown Tab, Furthermore, the final static pre-trained model to initialize our dynamic model. The total training time takes 6 days with 8 NVIDIA A100 80 GB GPUs. We use gsplat (Ye et al. 2025) as the 3DGS implementation.
Inference. One forward pass through the video diffusion model takes 12 minutes. As video models become faster, especially with progress in distillation into few-step models, this number is expected
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to decrease drastically with orthogonal improvements by the video generation community. Our 3DGS decoder takes only 3 seconds to generate 3D Gaussians from video latents. Lastly, we can render the 3D Gaussians in real-time after generation from novel viewpoints, where rendering 704x1280 frame takes 18 ms.
Efficiency and number of input trajectories. Our approach is trained to handle a variable number of input camera trajectories. During training, we randomly vary the number of trajectories between 1 and 6, enabling the model to generalize any number of trajectories within this range at inference time. While we primarily use 6 trajectories in our experiments to maximize view coverage, the model can operate with as few as a single trajectory.
Importandly, using fewer trajectories allows avoiding any computational overhead beyond standard camera- contra-controlled video generation. The 3DGS decoder requires only second for single trajectory and 3 seconds for six trajectories to produce 3D Gaussians, which is comparable to decoding RGB frames in 2D video generation models. Using additional trajectories therefore provides a controllable trade-off between view coverage and inference speed, without affecting reconstruction quality when fewer trajectories are used.
Dataset generation cost. The training dataset is generated fully automatically, without manual screen- ing or automated filtering, making the approach highly scalable. Dataset generation is dominated by the cost of the underlying video diffusion model, which currently takes 12 minutes per trajectory single NVIDIA A100 GPU, As a result, generating one training sample with 6 trajectories takes 72 minutes.
For the 3D dataset, we generate 59,031 scenes with 6 camera trajectories each, corresponding to approximately 71,000 GPU hours. For the 4D dataset, generate 7,378 scenes with 12 trajectories each, corresponding to approximately 18,000 GPU hours. This data generation process is performed once, offline, after which any number of models can be trained the resulting dataset.
Collecting real-world data with comparable diversity is practically infeasible, especially for the 4D case, which would require multiple cameras synchronized in time. We fully open-source the generated datasets to facilitate future research. As video generation models continue to become faster, the cost of dataset generation expected to decrease substantially. In contrast, training the 3DGS decoder itself is relatively inexpensive, requiring 6 days on 8 NVIDIA A100 GPUs, whereas related approaches often require multi-node training.
Subsampling Gaussians. Generating pixel-aligned 3D Gaussians scales linearly with each spatial and temporal resolution. Our generated scenes represent 726 frames at 704 x 1280 resolution, leading to 654,213, 120 per-pixel Gaussians. Hence, we only generate one 3D Gaussian per 8 x 8 spatial neighborhood, reducing the total number of 3D Gaussians by a factor of 64, i.e., 10,222,080. Moreover, our opacity-based pruning further reduces the number to 2,044,416, making the output more compact.
Joint Transformer-Me Mamba blocks. We use the reconstruction block design introduced in Long- LRM (Zwen et al.] 2024). i.c., one block consists of one Transformer layer followed by seven Mamba-2 layers. We repeat the block twice get 16 layers with 512 hidden dimensions. Note that our network is significantly smaller than the one used in Long-LRM (Ziwen et al.) 2024) or Wonderland (Liang et al.) 2025a), which use 24 layers with 1024 hidden dimensions. While pure Transformers are typically more powerful, they are significantly slower in training and inference than Mamba2. Since we trained both configurations within the same training budget, we hypothesize that transformer-only blocks would need considerably more training iterations to fully catch up in rendering quality.
Camera encoding, We use Plücker embeddings for camera encoding, following prior works (Kant al. 2024 Zhang et al. 2024e). The embeddings are computed at full spatial and temporal resolution and then projected into the video latent space using the pre-trained RGB encoder. Since the RGB encoder expects 3 input channels but Plücker embeddings are 6-dimensional, we separately encode the 3-dimensional ray directions and the 3-dimensional cross product of ray directions and origins. The resulting latent encodings are then concatenated along the channel dimension.
Time encoding. Similarly, to make the I-dimensional time input compatible with the 3-channel input expected by the RGB encoder, we concatenate a 2-dimensional sinusoidal embedding to the original time value along the channel dimension. We then replicate these values across the spatial dimensions
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Table Progressive training setup.
Stage
H x W
L
V
S
Steps
Statie
176x320
17
1
17
10k
176x320
49
49
2.5k
3
352x640
49
1
49
2.5k
4
724x1280
49
1
49
2.5k
5
704x 1280
121
1
57.5k
704x 1280
121
Dynamic
704x 1280
121
before encoding the time into the video latent space. This approach allows to independently encode both the source and target times into the compressed latent representation.
Dynamic data augmentation. We visualize dynamic data augmentation procedure for two example camera trajectories in Fig. / The procedure creates pairs of supervision views that cover the scene from the same motion state but different extreme viewpoints.
Zoom-Our Trajectory
Zoom-In Trajectory
Time
Original
Augmented
Original
Augmented
Figure 9: Dynamic data augmentation videos. We augment the supervision data with a motion- reversed video, ensuring that each timestep is observed from the full spatial coverage, thereby preventing low opacity artifacts in the early timesteps. We show two example trajectories, i.c., zoom-out and zoom-in, and visualize their corresponding augmented videos. The augmented videos are flipped in their camera motion.
Multi-view consistency. All camera trajectories are generated independently. but are rendered from shared underlying 3D/4D cache. In our framework, the video generation backbone (GEN3C) models scene memory through this cache, enabling camera-controlled video synthesis with implicit scene consistency across trajectories.
While this shared cache already provides degree of multi-view coherence, further improvements are possible by integrating more advanced memory mechanisms developed for long-form video generation. Recent approaches such Context as Memory (Yu et al. (2025) explicitly retrieve and reuse scene information over extended temporal horizons, and could further strengthen multi-view consistency in multi-trajectory setting. We leave the integration of such memory mechanisms for future work.
D ADDITIONAL EVALUATION
D.1 ADDITIONAL BASELINE DETAILS
We additionally compare our model against BTimmer (Liang et al. 2025b). recent approach for joint 3D and 4D reconstruction from posed images videos. We reached out to the authors of BTimer to conduct experiments for us using the same scenes as Lyra. Since BTimer is purely regression based, integrate it with our GEN3C (Ren et al. 2025) video diffusion backbone. Specifically,
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Blimer (GEN3C)
Ours
Figure 10: Image-to-3DGS Comparison. We compare our method with BTimer (GEN3C) and visualize five views from generated scenes. observe significantly fewer artifacts and higher fidelity.
Table 4: Comparison with pixel-space Long-IRM (GEN3C) baseline. Quantitative results on REIOK, DL3DV, and Tanks& Temples datasets.
Dataset
Method
PSNR
SSIM f LPIPS
Long-LRM (GEN3C)
18.45
0.653
0.378
RE10K
Ours
21.79
0.752
0.219
Long-LRM (GEN3C)
17.83
0.499
0.439
DL3DV
Ours
20.09
0,583
0,313
Long-LRM (GEN3C)
0.491
0.482
T&T
Ours
19.24
0.570
0.336
same camera trajectories as in our model and provide RGBBdecoded videos from GEN3C input to BTimer. BTimer operates in pixel space and requires 12 input images. However, as discussed in the main paper, pixel-space models such as BTimer run out of memory when directly using the 726 high-resolution frames generated by GEN3C. To address this, we uniformly subsample GEN3C frames. For static evaluations, select 12 frames that evenly cover the available viewpoints. For dynamic evaluations, we sample 11 frames to span the viewpoint range and additionally include the bullet-time frame corresponding the motion being reconstructed. This ensures that the target motion state is always present as a reference. We denote this integrated baseline as BTimer (GEN3C), i... Biimer combined with GEN3C.
D.2 ADDITIONAL STATIC 3D EVALUATION
Long-LRM GEN3C We compare against pixel-space baseline that combines GEN3C with Long-LRM (Zwen et al. 2024). In this setting, GEN3C serves the video generation backbone, while Long-LRM performs 3D reconstruction directly in pixel space. Since Long-LRM cannot process hundreds of generated frames, uniformly subsample the GEN3C output before using it as input. Quantitative comparisons are reported in Table
BTimer GEN3C For the task of image 3DGS generation, show qualitative comparisons with BTimer (GEN3C) in Fig. 10 and qualitative results in Tab. 6aa We observe higher quality and less artifacts for method.
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Table 5: CLIP and IQA metrics. Semantic alignment evaluated with CLIP (ViT-B/32, VIT-B/16,
ViT-L/14; higher is better) and perceptual image quality evaluated with NIQE (lower is better).
Method
CLIP-B/32 BBP-B/221 f CLIP-B/161 CLIP-L/141
NIQE
Long-LRM (GEN3C)
21.10
20.31
16.43
19.70
Ours
32.58
32.38
27.24
11.34
Table 6: Quantitative results static and dynamic Lyra datasets.
Method
PSNR t
SSIM
LPIPS +
Method
PSNR
SSIM f LPIPS
BTimer (GEN3C)
16.32
0,580
0.427
BTimer (GEN3C)
20.29
0.687
0.315
Ours
24.92
0.834
0.183
Ours
23.07
0.779
0.231
(a) Comparison on static Lyra dataset.
(b) Comparison on dynamic Lyra dataset.
CLIP and IQA metries. We evaluate our method using additional semantic and perceptual quality metrics. For semantic alignment, we report CLIP scores using three variants: ViT-B/32, ViT-B/16, and ViT-L/14, where higher values indicate better text-image alignment. For image quality assessment, we compute the Naturalness Image Quality Evaluator (NIQE), where lower values correspond to better perceptual quality. Tab. 3 reports the results and compares our method with the GEN3C + Long-LRM baseline. Our approach consistently achieves higher CLIP scores across all variants and substantially lower NIQE values, indicating improved semantic consistency and perceptual image quality.
D.3 DYNAMIC 3D EVALUATION
BTimer (GEN3C) comparison. For dynamic 3D evaluation, compare our method with BTimer (GEN3C), as discussed in Sec. D.I on 100 out-of-distribution videos from our dynamic Lyra dataset. We crop the video 512 512 for fair comparisons, BTimer was mainly trained on that resolution. We show results in Tab, 6b and observe that our method significantly outperforms BTimer (GEN3C).
D.4 DEPTH VISUALIZATION
We visualize depths from our generated 3DGS in Fig. geometries without sacrificing visual quality.
Using depth supervision prevents flat
Ours
Ours (w/o depth loss)
Figure 11: Depth loss ablation. We visualize 3DGS renderings and corresponding depth maps. Using the depth loss as additional supervision prevents flat geometries without sacrificing visual quality.
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opp
Figure 12: Robot Simulation. We visualize a frame robot simulation within the Isaac Sim 5.0 simulation framework that takes generated 3DGS scene from our method as input.
E ROBOT SIMULATION
One our primary motivations for building diverse 3D generator is ability to synthesize simulation environments for autonomous agents. To this end, we develop end-to-end pipeline: we first generate 3D scenes from text with our model, export the 3D Gaussians as a -ply file, and convert the -lly files into .usdz format. We use 3DGUT (Wu al. 2025a) and its export function to create .usdz files. The resulting ,usdz file can then be imported into the NVIDIA Isaac robot development platform physically based virtual environment. Al-based robots can be trained and tested under diverse conditions within these environments. We present early demo results on our supplementary webpage and screenshot of the interface in Fig. [2
F RELATED WORK
In this section, will discuss additional related work. We highlight the differences to previous works CAT3D (Gao et al. 2024b) and Bolt3D (Szymanowicz et al. 2025b) in Fig. [3
3D generation. Early approaches to 3D generation primarily focused single object categories, extending GANs into the 3D domain by leveraging neural renderers as an inductive bias Vries
al., 2021 Chan et 2022 Or-El et al. 2022; Schwarz al. 2022 Bahmani al. 2023b) With
the introduction of CLiP-based supervision (Radford et al. 2021), the field advanced toward more flexible and diverse
creation, enabling both text-driven generation and editing (Chen al.
2018 Jain et al., 2022 (anghi et al. 2022: Jetchey 2021 Gao et al. 2023 Wang et al.
2022) More
recently, diffusion models have signifcantly improved quality by replacing CLIP guidance with Score
Distillation Sampling (SDS) (Poole et al. 2023 Wang et al. 2023b
Lin et al.
2023a Chen et al.
20236 [iang al. 2023 Wang et al.] 2003a] et 2024e He et al 2024b)
et 2024
al. 2024b
Yu al. 2023 Katzir al. 2024 Lee et al. 2024a Sun al.
(024a).
To further enhance structural coherence, several methods enforce multi-view consistency by generat-
ing scenes from different perspectives (Lin et al. 2023b Liu et al. 2023 Shi et al.) 2024 Feng et al.
2024a Liu et al. 2024a Kim et al. (2023) Voleti et al. 2024 Höllein et al. 2024; Tang et al. 2024c
Gao et al. 2024b Wang et al. [2025t] Kant et al.) 2025 Yuan al. [2025] Ren et al. (022b) while
others adopt iterative inpainting as a strategy for scene synthesis (Höllein et al. (2023] Shriram et all
2024). Another line of work explores lifting 2D images into 3D using NeRF (Mildenhall et al.) 2020
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CAT3D
Bolt3D
Ours
3D Gaussians
3D Gaussians
3D Gaussians
Optimization
3D Gaussian Heaad
Multi-View Images
Multi-View Images and Pointmaps
Decoder (RGB)
Decoder (RGB)
Decoder (Geu)
Decoder (3DGS)
Multi-View
Multi-
Multi-
Image Diffussio Model
Image Diffusion Model
Vidco Diffusion Model
Encoder (RGB)
Encoder (RGB)
Encuder (RGB)
Input Image (s)
Input Image (s)
Input Image (s)
(a)
(b)
(c)
Figure 13: Approaches for 3D Generation. CAT3D (Ga et al. 2024b) proposed multi-view image diffusion model that outputs images from novel viewpoints; subsequently, the images are reconstructed into 3D Gaussians using optimization. Bolt3D (Szymanowicz et al.) (025b) fine-tunes CAT3D to output pointmaps using a geometry autoencoder; instead of relying on optimization, the 3D scene is predicted with feed-forward 3D Gaussian head. In contrast, our work builds upon a pre-trained camera-controlled video diffusion model and directly decodes the multi-viww video latents into 3D Gaussians.
3D Gaussian Splatting (erbl et al. 2023), or mesh-based representations coupled with diffusion
models (Chan et al. 2023| lang et 2023a Gu et al. 2023 Liu et al. 2024c Yoo et al. 2023
Tewari et al. 2023) Qian et al. 2024b Long et al. 2024 Wan et al. 2024 Szymanowicz et al. 2023
Lu et al. 2024).
Feed-forward 3D models. Complementary efforts investigate fast, feed-forward techniques that directly predict 3D content from images or text inputs (Hong et al. [022]] Li al., 2024bb Xu et al.
2024e d Zhang et al. 2024a;
Han el al. 2024; Jiang & Wang, (2224] Xie et al.
2024a] Tang et al.
2024b] Tochilkin et al. 2024: Qian et al. 2024al Szymanowicz et al. 2024 2025a Liang et al. 2025a
Szymanowicz et al. 2025b Schwarz et al. 2025] Yang et al. 2025 Zhang et al. (022a)) However,
in contrast our method, these approaches remain limited to producing static 3D scenes. Other
methods focus on specific scenes such as faces (Kirschstein et al. 2025). Concurrent work (Liang
jet al. 2025b Xu et al. 2025) tackles real scenes but can not handle diverse generated scenes or large
viewpoint changes.
4D generation. Recent years have seen rapid progress in 4D generation, i.c., dynamic 3D scene
synthesis. Many approaches leverage input text prompts or images as guidance. Following the introduction of large-scale generative models for this task (Singer et al. 2023). subsequent works
have made notable advances in both visual fidelity and motion quality (Ren et al.
2023 Ling et al.
2024a
Bahmani et al. (002bb] Zheng et al. 2024b Gao et al. 2024a Yang et al.)
2024a Jiang et al.
2024b
Miao et 2024| Li al. 2024a Zhang al. 2024g] Yuan al. 2024 liang al. 2024a).
While text conditioning is widely used, alternative methods aim to lift 2D images or videos into
dynamic 3D scenes (Ren et al. [2023 Zhao al. 2023 Yin al. 2023 Pan al. [0024] Zheng et
2024b
Ling et | 2024al
Gao et al. |0244]] Zeng et al.
2024b] Chu et al. 2024] Wu et al.
[2024] Yang
al.
(024::Waag et al.
2024c Feng et al. [2024b;] (un et al.
2024c]
Zhang et al. [2024c Ren et al.
2024a
Lee et al. 2024b
Li et al. 2024c Van Hoorick et al.
2024 Uzolas et al.
2024 Chai et al.
2024
Liang et al. 2024|
al. 2024d| Xie al. 2024b] Li al.
2025a Wang et al., 2025e Zhu
et al.
2025 Zhou et al. 2025 Hu et al.) 2025 Park et al. (025). Other efforts explore incorporating
physics priors into generation pipelines (Lin et al.] 2024, Huang et al. 2024b Zhang et al. 2024f
Kiray al. (022), ) enhancing motion controllability with template-driven approaches (Zhang et al.
2024d| Sun et al. 2024b Chen et al. 2022b)) A parallel direction emphasizes compositional and
interactive 4D generation (Bahmani et al. 2024a Xu et al. 2024b Cao et al. 2024
Yu et al. 2024a
Zeng et al. 2024a Zhu et al. 2024)..
Another strand of research extends 3D GAN frameworks into the 4D setting by training directly on
2D video data (Bahmani et al. 2023a Xu et al. 2023). However, such methods are typically limited
by small-scale, single-category datasets, which constrain generalization. Moreover, most existing
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approaches remain object-centric, often neglecting background elements. As result, their overall visual fidelity lags behind the high photorealism achieved by recent video generation models, which method leverages.
Canera-conditioned video models. Recent progress has focused on incorporating camera control into video diffusion models. The pioneering work MotionCtrl (Wang et al. (2024g) introduced camera
conditioning by augmenting pre-trained video models (Chen et al., [2222 3
Blattmann et al. 2023)
with extrinsic matrices. Subsequent efforts (He al. [2024a] Xu
[222cc] Kuang al. [0224] Ju
et al. 2025 He et al. 2025 Li et al. 2025c Wang et al.) 2025c 2024d Lei el al. 2025] Liu et al.
2025 enhanced conditioning by representing cameras using Plücker coordinates, while others use
follow MotionCtul to simply use poses flattened (Wang et al., 2025b). Another line of research (Hu
et al. 2024, Xiao et al.
2024 Ling al. 2024c] Hou al. 2024) enabled camera motion control
without introducing additional trainable parameters. Despite these advances, all of the above rely on
U-Net-based architectures. Other approaches use low-rank approaches for camera control ZZang
al.
2025b).
Several recent works continue to extend U-Net-based approaches for camera control (Zhao et al. 2024
Xu et al. 2024a; Zheng et al. 2024a Zhang et al. 2024b Yu et al.] 2024b; Lei et al. 2024). while others begin exploring diffusion transiormers. Cheong et al. aChonne et al. 2024) propose one such transformer- based framework, though its scene and visual quality remain limited. DimensionX(SSu et al. 2024d introduces joint space-time control in diffusion transformers, but relies on pre-defined,
non-continuous camera trajectories. Other works (Huang et al.. 2024a Mai al. 2025 Jiang et al.
2025b) instead explore pose and geometry estimation with a video DiT. CAT4D (Wu et al. 2025b)
proposes multi-view video diffusion model derived from fine-tuning multi-view backbone. Large- scale synthetic data generation further boosts performance, as demonstrated by SynCamMaster (Bai al. (2055b) and ReCamMaster (Bai et al. (2025), which achieve strong results on camera camnera-controlles video generation using transformer-basse architectures. Finally, 4DiM (Watson et al. (024) trains space-time di liffusion model from scratch for novel view synthesis from single input image. Another line of work edits camera movements of videos with underlying motion using camera retargeting (Seo) ctt al., 2025) Ma et al. 2025). One popular direction has been to use a spatio-temporal cache, c.g.,
using point clouds optical flow, to condition the camera-controlled video generation (Wu et al.
2025c Ren et al. 2025 Wang et al. 2025d Cao et al. 2025 YU et al. 2025
Jin et al.
2025
Xing
al.
2025
Yan al. 2025 Li et al.
2025b Gu a..
2025
31


Reviews:
[{"review_id": "6tTAd1uMgO", "reviewer": "Reviewer_hhrs", "summary": "This paper introduces Lyra, a novel framework for generating explicit 3D (and dynamic 4D) scene representations from a single image or video. The key contribution is a self-distillation method that distills the implicit 3D knowledge from a pre-trained, camera-controlled video diffusion model (the \"teacher\") into a feed-forward 3D Gaussian Splatting (3DGS) decoder (the \"student\"). This approach cleverly avoids the need for large-scale, real-world multi-view 3D datasets by using the video model's RGB output to supervise the 3DGS decoder, both operating from a shared video latent. The framework is also effectively extended to dynamic 4D scene generation from a single monocular video, demonstrating strong performance and generalization.", "questions": "Please refer to the weakness part.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Novel Self-Distillation: The core idea of using a pre-trained video diffusion model as a \"teacher\" to supervise a 3DGS \"student\" decoder is highly novel. This latent-space distillation is an elegant solution to the 3D training data bottleneck, effectively bridging the gap between powerful 2D generative models and explicit 3D representations.\n\n2. SOTA Performance and Representation: The method achieves state-of-the-art quantitative results on standard benchmarks (RealEstate10K, DL3DV, Tanks-and-Temples) for single-image-to-3D generation. By directly outputting a 3DGS representation, the model produces a geometrically consistent, explicit 3D scene that supports real-time rendering, which is a significant advantage over 2D-only video models.\n\n3. Efficient Latent-Space Operation and 4D Extension: Operating the 3DGS decoder in the VAE's latent space allows the model to efficiently process a very large number of synthesized views (726 views per scene), overcoming the memory and compute bottlenecks of pixel-space reconstruction methods. The extension to 4D dynamic scenes from a single video, supported by a clever \"dynamic data augmentation\" strategy, is a substantial and well-executed addition.", "weaknesses": "1. Dependency on Teacher Model Quality: The paper's conclusion rightfully states that the output quality is \"bounded by the capacity of our camera-controlled video diffusion model.\" This is a strong dependency. Any artifacts, flickering, or 3D inconsistencies generated by the teacher (GEN3C) will be directly distilled into the final 3DGS representation. The paper does not fully explore how robust the framework is to such failures in the teacher model.\n\n2. Reliance on External Depth Estimation: The paper notes that using only RGB loss \"often produces flattened geometry,\" and thus adds a scale-invariant depth loss ($\\mathcal{L}_{depth}$) supervised by an off-the-shelf depth estimator (ViPE). While effective, this means the method is not purely self-distilled from the video model; it relies on another pre-trained model for geometric guidance. This slightly weakens the claim of distilling 3D knowledge solely from the video model's multi-view consistency.\n\n3. Minor Terminology Nitpick: The term \"self-distillation\" is used, but the framework is more accurately a form of cross-modal distillation: from a 2D decoder (the VAE's RGB head) to a 3D decoder (the new 3DGS head). In classic self-distillation, a large model teaches a smaller model of the same architecture. This is a minor semantic point and does not detract from the method's novelty.", "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": "eq80w4guNu", "reviewer": "Reviewer_Yx9E", "summary": "This paper introduces Lyra, a pipeline for generative 3D scene reconstruction and text-driven scene editing under sparse and noisy real-world mobile captures. The method jointly optimizes camera, geometry, and appearance and supports object removal, replacement, and global stylistic edits. It integrates latent diffusion priors with real-world scene scale and spatial consistency constraints, demonstrated on a diverse dataset including indoor and outdoor environments (e.g., apartment, temple, garden scenes) where real mobile scans contain artifacts such as railings and scaffolds.", "questions": "Please refer to the weakness part.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. The method targets real mobile scans with imperfect geometry, lighting, and camera poses, a valuable problem space. Example scenes include cluttered apartments and outdoor temple/garden scans with occluders (e.g., railings, scaffolding).\n\n2. The method can recover missing background structure when occluded (e.g., walls behind furniture) and maintain accurate room layout.\n\n3. Lyra enforces realistic scale and physical spatial consistency across generations.", "weaknesses": "1. While results demonstrate quality, more specifics on training/inference time would help adoption clarity.\n\n2. Some failure modes still visible. E.g., subtle texture drift in larger structural edits, though still competitive and reasonable under sparse-view settings.", "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": "Jev5TxvsJv", "reviewer": "Reviewer_tRus", "summary": "This paper introduces Lyra, a self-distillation framework designed to distill the implicit 3D knowledge from video diffusion models into an explicit 3D Generative Scene (3DGS) representation. The process begins by collecting video latent data, which includes multiple camera trajectories obtained from a pretrained camera-controlled video diffusion model. Using this dataset, the authors build upon long-LRM to transform the video latent representations into 3DGS. Additionally, the approach can be extended to dynamic content by generating 3DGS on a per-frame basis. Experimental results demonstrate that the proposed framework achieves state-of-the-art performance in both static and dynamic 3D scene generation.", "questions": "1. What is the main difference between this method and Wonderland, which also generates video latent representations first and uses latent-LRM for 3DGS reconstruction?\n   \n2. Compared to previous methods, what accounts for the performance improvements? Is it due to the camera-controlled video diffusion model being superior to earlier versions or denser camera viewpoints? \n\n3. What is the result of directly feeding the generated pixels from Gen3C into long-LRM, despite their efficiency? This could be a baseline.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 2, "strengths": "1. The paper is well-written and easy to follow, providing sufficient implementation details.\n2. The method is straightforward, and the implementation is well executed. It allows for the immediate transformation of the generated video into a 3DGS representation and enables dynamic scene reconstruction from the video generation model.", "weaknesses": "1. The technical contribution is somewhat limited. It presents a straightforward combination of video diffusion and long-LRM. While long-LRM can accept raw images as input, the proposed method specifically utilizes video latent representations. This technique has already been implemented in Wonderland [1]. \n\n2. For dynamic scene reconstruction, utilizing frame-wise 3DGS may result in temporal inconsistencies, as demonstrated in the provided video.\n\n[1] Wonderland: Navigating 3D Scenes from a Single Image", "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": "5AB5p5bD7E", "reviewer": "Reviewer_J3MU", "summary": "This paper introduces a self-distillation strategy based on a camera-controlled video diffusion model and only needs to train a 3DGS decoder directly operating in the latent space of the video model. In this way, the 3DGS decoder is supervised by the original RGB decoder of the video model, which only needs an input image and a sampled camera trajectory instead of captured real-world multi-view images. 4D generation can be easily extended through time conditioning modules within the 3DGS decoder.", "questions": "Please see the weaknesses.\n\nSince the motivation of the paper is very important, my initial score is 6. If my concerns can be properly addressed, I will actively consider increasing the score.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The motivation of the paper is clear and important. Using synthetic data instead of real multi-view data can obviously provide greater diversity.\n2. The overall paper is easy to read, with a very clear logic and structure.\n3. The paper provides extensive experiments to demonstrate the effectiveness of the proposed method.", "weaknesses": "1. I have some doubts about the efficiency of the proposed approach. It is generally acknowledged that video generation models are still quite time-consuming, and this paper’s method additionally requires multiple trajectories. However, I did not see any reported statistics on the dataset generation time, nor whether additional manual screening or automated filtering is needed.\n\n2. Another key concern lies in multi-view consistency. Are the videos for different trajectories generated independently? If so, how is multi-view consistency ensured? If not, wouldn’t that further reduce efficiency and make the approach difficult to apply in practice? I have the same question regarding the *video-to-4D* setting.\n\n3. In the quantitative experiments, I believe that monocular reconstruction or single image-to-3d generation tasks are difficult to evaluate using reconstruction metrics, and the authors did not provide corresponding qualitative comparisons. Moreover, since datasets like DL3DV contain complex and diverse trajectories, while Lyra generates 3D scenes along fixed trajectories, how does Lyra achieve the best quantitative results under such conditions?", "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)