Title: SceneScape: Text-Driven Consistent Scene Generation
Venue: NeurIPS 2023
Accepted: yes
Keywords: Computer Vision

Image & Video Editing

Video Generation

Perpetual View Generation

Texture Synthesis & Inpainting
Abstract: We present a method for text-driven perpetual view generation -- synthesizing long-term videos of various scenes solely, given an input text prompt describing the scene and camera poses. We introduce a novel framework that generates such videos in an online fashion by combining the generative power of a pre-trained text-to-image model with the geometric priors learned by a pre-trained monocular depth prediction model. To tackle the pivotal challenge of achieving 3D consistency, i.e., synthesizing videos that depict geometrically-plausible scenes, we deploy an online test-time training to encourage the predicted depth map of the current frame to be geometrically consistent with the synthesized scene. The depth maps are used to construct a \emph{unified} mesh representation of the scene, which is progressively constructed along the video generation process. In contrast to previous works, which are applicable only to limited domains, our method generates diverse scenes, such as walkthroughs in spaceships, caves, or ice castles.

SceneScape: Text-Driven Consistent Scene Generation

Abstract

We present a method for text-driven perpetual view generation - synthesizing long-term videos of various scenes solely from an input text prompt describing the scene and camera poses. We introduce a novel framework that generates such videos in an online fashion by combining the generative power of a pretrained text-to-image model with the geometric priors learned by a pre-trained monocular depth prediction model. To tackle the pivotal challenge of achieving 3D consistency, i.e., synthesizing videos that depict geometrically-plausible scenes, we deploy an online test-time training to encourage the predicted depth map of the current frame to be geometrically consistent with the synthesized scene. The depth maps are used to construct a unified mesh representation of the scene, which is progressively constructed along the video generation process. In contrast to previous works, which are applicable only to limited domains, our method generates diverse scenes, such as walkthroughs in spaceships, caves, or ice castles. Project page: https://scenescape.github.io/

1 Introduction

By observing only a single photo of a scene, we can imagine what it would be like to explore it, move around, or turn our head and look around - we can envision the 3D world captured in the image and expand it in our mind. However, achieving this capability computationally - generating a long-term video that depicts a plausible visual world from an input image - poses several key challenges. One of the main challenges is ensuring that the synthesized content is consistent with a feasible 3D world. For example, it must correctly depict parallax effects and occlusion relations of different objects in

the scene. Additionally, to synthesize new content, a strong prior about the visual world and how it would appear beyond the current field of view is necessary. Finally, the generated content should appear smooth and consistent over time.

Previous methods have tackled the task of perpetual view generation for specific domains, for example, synthesizing landscape flythroughs [e.g., 5, 7, 28, 29], or bedrooms walkthroughs [e.g., 45, 47]. These methods involve large-scale training on videos or images from the target domain, which limits their use. In this work, inspired by the transformative progress in text-to-image generation, we take a different route and propose a new framework for text-driven perpetual view generation - synthesizing long-range videos of scenes solely from free-vocabulary text describing the scene and camera poses. Our method does not require any training data but rather generates the scene in a zero-shot manner by harnessing the generative prior learned by a pre-trained text-to-image diffusion model and the geometric priors learned by a pre-trained depth prediction model.

More specifically, given an input text prompt and a camera trajectory, our framework generates the video in an online fashion, one frame at a time. The text-to-image diffusion model is used to synthesize new content revealed as the camera moves, whereas the depth prediction model allows us to estimate the geometry of the newly generated content. To ensure that the generated scene adheres to a feasible 3D geometry, our framework estimates a unified mesh representation of the scene that is constructed progressively as the camera moves in the scene. Since monocular depth predictions tend to be inconsistent across different frames, we finetune the depth prediction model at test time to match the projected depth from our mesh representation for the known, previously synthesized content. This results in a test-time training approach that enables diverse, 3D-consistent perpetual view generation.

To summarize, our key contributions are the following:

The first text-driven perpetual view generation method, which can synthesize long-term videos of diverse domains solely from text and a camera trajectory.

The first zero-shot/test-time 3D-consistent perpetual view generation method, which synthesizes diverse scenes without large-scale training on a specific target domain.

Achieving 3D-consistent generation by progressively estimating a unified 3D representation of the scene.

We thoroughly evaluate and ablate our method, demonstrating a significant improvement in quality and 3D consistency over existing methods.

2 Related Work

Perpetual view generation. Exploring an 'infinite scene' dates back to the seminal work of Kaneva et al. [24], which proposed an image retrieval-based method to create a 2D landscape that follows a desired camera path. This task has been modernized by various works. Different methods proposed to synthesize scenes given as input a single image and camera motion [e.g., 21, 26, 28, 29, 45, 46, 56, 58, 63]. For example, synthesizing indoor scenes [26, 45, 46, 58], or long-range landscapes flythroughs [28, 29]. These methods have demonstrated impressive results and creative use of data and self-supervisory signals [e.g., 5, 7, 11, 12, 28, 45]. However, none of these methods considered the task of text-driven perceptual view generation and rather tailored a model for a specific scene domain . Thus, these methods require extensive training on a large-scale dataset to learn proper generative and geometric priors of a specific target scene domain.

3D-Consistent view synthesis from a single image. Various methods have considered the task of novel view synthesis from a single input image. A surge of methods has proposed to utilize a NeRF [35] as a unified 3D representation while training on a large dataset from a specific domain to learn a generic scene prior [22, 60, 62]. Other 3D representations have been used by various methods, including multi-plane images [57, 66], point clouds which hold high-dimensional features [46, 58], or textured-mesh representation [20]. In Jain et al. [22], the task is further regularized using the semantic prior learned by CLIP [41], encouraging high similarity between the input image and each novel view in CLIP image embedding space. All these methods require training a model on specific domains and cannot produce perpetual exploration of diverse scenes.

Projection to

Text-to-Image

Inpainting Model

Monocular

depth

estimation

Updated Mesh

Starting Mesh

"walkthrough a museum,

masterpiece"

It+1

9

→Ldisp(D1,Dt+1）

Dt+1

3D-Aware image generation. Related to our task is 3D-aware image synthesize, which typically involves incorporating a 3D representation explicitly into a generative model, e.g., a NeRF representation [8, 9, 16, 36, 50], or neural surface renderer [38]. These methods have shown impressive results on structured domains (e.g., cars or faces), but deploying them for diverse scenes is extremely challenging and often requires additional guidance [13], or heavy supervision, e.g., large-scale data that contains accurate camera trajectories, which can be obtained reliably mainly for synthetic data [4]. We aim for a much more diverse, flexible, and lightweight generation of arbitrary scenes.

Text-to-video generation and editing. Our work follows a broad line of works in the field of text-tovideo synthesis and editing. There has been great progress in developing text-to-video generative models by expanding architectures to the temporal domain and learning priors from large-scale video datasets [e.g. 15, 18, 32, 51]. Nevertheless, these models are still in infancy, and are lagging behind image models in terms of quality (resolution and video length). On the other side of the spectrum, a surge of methods proposes to tune or directly leverage a 2D text-image model for video editing tasks [e.g., 3, 6, 40, 59, 65]. We also follow this approach in conjunction with generating an explicit 3D scene representation.

Text-to-3D Generation and Editing. Recently, language-vision pre-trained models [41, 49], and differentiable renderers [30, 35] have been utilized for text-driven 3D content generation. For example, CLIP-based losses have been used for the text-driven stylization of a given surface mesh [34] or for synthesizing a NeRF from an input text [23]. Taking this approach further, Poole et al. [39] distills the generative prior of a pre-trained text-to-image diffusion model by encouraging the optimized NeRF to render images that are in the distribution of the diffusion model [49]. Singer et al. [52] extends this approach to a 4D dynamic NERF and optimizes using a pre-trained text-to-video model. Nevertheless, it is limited to object-centric scenes while we generate long-term walkthrough-type videos. On the other hand, Chang et al. [10] and Ma et al. [33] construct the semantic scene graph from natural language and retrieve the required elements from the 3D objects databases.

Finally, in a very recent concurrent work, Text2Room [19] proposed a similar approach to ours to generate 3D scenes based on textual prompts. However, this work focuses specifically on creating room meshes and tailors both the camera trajectory, prompts and mesh rendering for this purpose. In contrast, we use the mesh representation only as a tool to achieve videos of diverse scenes.

3 Method

The input to our method is a text prompt P , describing the target scene, and a camera trajectory denoted by { C i } T i =1 , where C i ∈ R 3 × 4 is the camera pose of the i th frame. Our goal is to synthesize a long-term, high-quality video depicting the desired scene while adhering to the camera trajectory.

Our framework, illustrated in Fig. 2, generates the video one frame at a time, by leveraging a pretrained text-to-image diffusion model [48], and a pre-trained monocular depth prediction model [42, 43]. The role of the diffusion model is to synthesize new content that is revealed as the camera moves, whereas the depth prediction model allows us to estimate the geometry of the newly generated content. We combine these two models through a unified 3D representation of the scene, which ensures geometrically consistent video generation. More specifically, at each time step t , the new synthesized content and its predicted depths are used to update a unified mesh representation M , which is constructed progressively as the camera moves. The mesh is then projected to the next view C t +1 , and the process is repeated.

Since neither the depth prediction model nor the inpainting model are guaranteed to produce consistent predictions across time, we finetune them both to match the content of our unified scene representation for the known, previously synthesized content. We next describe each of these components in detail.

3.1 Initialization and Scene Representation

We denote the pre-trained text-to-image inpainting model by ϕ , which takes as input a text prompt P , a mask M , and a masked conditioning image I t M :

The first frame in our video I 0 is generated by sampling an image from ϕ , using P without masking (all-ones mask). The generated frame is then fed to a pre-trained depth prediction model g , which estimates D 0 the depth map for I 0 , i.e., D 0 = g ( I 0 ) .

With the estimated depth and camera poses, a naïve approach for rendering I t +1 is to warp I t to C t +1 , e.g., using 2D splatting [37]. However, even with consistent depths, rendering a 3D-consistent scene is challenging. In typical camera motion, multiple pixels from one frame are mapped into the same pixel in the next frame. Thus, the aggregated color needs to be selected carefully. Furthermore, frame-to-frame warping lacks a unified representation, thus, once the scene content gets occluded, it will be generated from scratch when it gets exposed again

To address these challenges, we take a different approach and represent the scene with a unified triangle mesh M = ( V , F , E ) which is represented by a tuple of vertices (3D location and color), faces and edges, respectively. We initialize M 0 by unprojecting ( I 0 , D 0 ) . After the first frame is generated, each synthesis step consists of the following main stages:

3.2 Project and Inpaint

The current scene content, represented by the unified mesh representation M t , is projected to the next camera C t +1 :

producing a mask M t +1 of the visible content in C t +1 , a masked frame I t M +1 , and a masked depth map D t M +1 . Given M t +1 and I t M +1 , we now turn to the task of synthesizing new content. To do so, we apply ϕ using Eq. 1.

3.3 Test-time Depth Finetuning

We would like to estimate the geometry of the new synthesized content and use it to update our unified scene mesh. A naïve approach to do so is to estimate the depth directly from the inpainted frame, i.e., D t +1 = g ( I t +1 ) . However, monocular depth predictions tend to be inconsistent, even across nearby video frames [27, 31, 64]. That is, there is no guarantee the predicted depth D t +1 would be well aligned with the current scene geometry M t . We mitigate this problem by taking a test-time training approach to finetune the depth prediction model to be as consistent as possible with the current scene geometry. This approach is inspired by previous methods [27, 31, 64], which encourage pairwise consistency w.r.t optical flow observations in a given video. In contrast, we encourage consistency between the predicted depth and our unified 3D representation in the visible regions in frame t +1 . Formally, our training loss is given by:

where D t M +1 , M t +1 are given by projecting the mesh geometry to the current view (Eq. 2). To avoid catastrophic forgetting of the original prior learned by the depth model, we revert g to its original weights at each generation step.

3.4 Test-time Decoder Finetuning

Our framework uses Latent Diffusion inpainting model [48], in which the input image and mask are first encoded into a latent space z = E LDM ( M t , I t M ) ; the inpainting operation is then carried out in the latent space, producing ˆ z , and the final output image is given by decoding ˆ z to an RGB image: D LDM (ˆ z ) . This encoding/decoding operation is lossy, and the encoded image is not reconstructed exactly in the unmasked regions. Following Avrahami et al. [2], for each frame, we finetune the decoder as follows:

The first term encourages the decoder to preserve the known content, and the second term is a prior preservation loss [2]. As in Sec. 3.3, we revert the weights of the decoder to its original weights.

3.5 Online Mesh Update

Given the current mesh M t , and the inpainted frame I t +1 , we use our depth map and camera pose to update the scene with the newly synthesized content. Note that in order to retain the previously synthesized content, some of which may be occluded in the current view, we consider only the new content, unproject, and mesh it, adding it to M t . That is,

Finally, ˜ M t +1 is merged into the mesh:

In practice, we also add to the mesh the triangles used to connect the previous content to the newly synthesized one. See Sec. A.1 of appendix for more details.

3.6 Rendering

Rendering a high-quality and temporally consistent video from our unified mesh requires careful handling. First, stretched triangles may often be formed along depth discontinuities. These triangles encapsulate content that is unseen in the current view, which we would like to inpaint when it becomes visible in other views. To do so, we automatically detect stretched triangles based on their normals and remove them from the updated mesh.

Another issue arises when the content at the border of the current frame is close to the camera relative to the scene geometry. This content is typically mapped towards the interior of the next frame due to parallax. This often results in undesirable 'floating artifacts' where the revealed border regions are rendered with the existing distant content (see Fig. 3). We assume that such regions should be inpainted, thus we automatically detect and include them in the inpainting mask. See Sec. A.2 of appendix for more details.

4 Results

We evaluate our method both quantitatively and qual- itatively on various scenes generated from a diverse set of prompts, including photorealistic as well

"a dimly lit library, with rows upon rows of leather-bound books and dark wooden shelves'

"POV, haunted house, dark, wooden door, spider webs, skeletons"

" A grand, marble staircase spirals up to a vaulted ceiling in a grand entrance hall of a palace."

"simple museum, pictures, paintings, artistic, best quality, dimly lit"

"inside a castle made of ice, beautiful photo, masterpiece"

"walkthrough, sci-fi ship interiors, corridors,amazing quality, masterpiece, beautiful scenery, best quality"

"walkthrough, inside a medieval forge, metal, fire, beautiful photo, masterpiece"

as imaginary scenes. We generated 50-frame long videos, with a fast-moving camera motion, which follows a backward motion combined with rotations of the camera (see Sec. A of appendix for more details). Since our method represents the scene via a mesh, we focus mainly on indoor scenes. Synthesizing outdoor scenes would require careful handling in cases of dramatic depth discontinuities (as discussed in Sec. 5).

Figure 1 and Fig. 4 show sample frames from our videos. The results demonstrate the effectiveness of our method in producing high-quality and geometrically-plausible scenes, depicting significant parallax, complex structures, and various complex scene properties such as lighting or diverse materials (e.g., ice). Additional results, videos, and output depth maps are included in the Supplementary materials (SM).

(a) Full method

(b) w/o depth finetuning

(c) w/o decoder finetuning

Frame

Our Depth

COLMAP

(d) w/o mesh

4.1 Metrics

In all experiments, we use the following metrics for evaluation:

Depth consistency. We evaluate the depth consistency of a video by applying COLMAP, using known camera poses, and measuring the Scale-Invariant Root Mean Square Error (SI-RMSE) [14]:

where D o and D c are our output depth maps and COLMAP depths, respectively. We compute the SI-RMSE for each frame and report the average per video. To assess the completeness of the reconstruction, we also measure the percentage of pixels recovered by COLMAP, i.e., passed its geometric consistency test. In addition, we report the average reprojection error of COLMAP reconstruction, which indicates the consistency of the video with COLMAP's point cloud.

Pose accuracy. We measure the accuracy of the estimated camera trajectory. Specifically, we provide COLMAP only with intrinsic parameters and align the two camera systems by computing a global rotation matrix [61]. We measure the normalized mean accuracy of camera positions (in percentage, relative to the overall camera trajectory), and camera rotations (in degrees).

Visual quality. We use Amazon Mechanical Turk (AMT) platform for evaluating the visual quality of our videos compared to the competitors. We adopt the Two-alternative Forced Choice (2AFC) protocol, where participants are presented with two side-by-side videos, ours and the competitor's (at random left-right ordering) and are asked: ' Choose the video/image that has better visual quality, e.g., sharper, less artifacts such as holes, stretches, or distorted geometry '. Additionally, we compute the CLIP aesthetic score [1]-an image aesthetic linear predictor learned on top of CLIP embeddings.

Ours

VideoFusion

"Medieval castle"

"Candy house"

"Opulent hotel"

4.2 Ablation

Weablate the key components in our framework: (i) depth finetuning (Sec. 3.3), (ii) decoder finetuning (Sec. 3.4), and (iii) our unified mesh representation (Sec. 3.1). For the first two ablations, we apply our method without each component, and for the third, we replace our mesh with 2D rendering, i.e., warping the content from the current frame to the next, using a depth-aware splatting [37]. In addition, we consider the baseline of simple warp-inpaint, i.e., using splatting with the original estimated depth (w/o finetuning). We ran these ablations on 20 videos, using a set of 10 diverse text prompts. See Sec. B for the list of prompts and SM sample videos.

Table 1 reports the above COLMAP metrics (see Sec. 4.1). Our full method results in notably more accurate camera poses, denser reconstructions, and better-aligned depth maps. Excluding depth finetuning significantly affects the depth consistency (high SI-RMSE). Excluding decoder finetuning leads to high-frequency temporal inconsistency, which leads to higher reprojection error. Without a mesh, the reconstruction is significantly sparser and we observe a drop in all metrics. These results are illustrated in Fig. 5 for a representative video.

To assess visual quality, we follow the AMT protocol described in Sec. 4.1, where we ran each test w.r.t. one of the baselines. We collected 4,000 user judgments from 200 participants over 20 videos. As seen in Table 1, the unified mesh plays a major role in producing high-quality videos, where the judgments were ambiguous w.r.t w/o depth finetuning baseline. We hypothesize this is because this baseline still leverages the mesh representation, thus the visual artifacts are temporally consistent, making them more difficult to perceive.

4.3 Comparison to Baselines

To the best of our knowledge, there is no existing method designed for our task - text-driven perpetual view generation. We thus consider the most relevant methods to our setting:

VideoFusion (VF) [32]. A text-to-video diffusion model 2 , which takes as input a text prompt and produces a 16-frame long video at 256 × 256 resolution.

GEN-1 [15]. A text-driven video-to-video translation model, where the translation is conditioned on depth maps estimated from an input source video. Note that in our setting the scene geometry is unknown and is constructed with the video, whereas GEN-1 takes the scene geometry as input, thus tackling a simpler task.

Due to the inherent differences between the above methods, we follow a different evaluation protocol in comparison with each one of them.

4.3.1 Evaluating 3D-consistency

To compare to VF, we used the same set of prompts as in Sec. 4.2 as input to their model and generated 1000 videos. We evaluate the 3D consistency of VF's videos and ours, using the same metrics as in Sec. 4.2, where we used COLMAP without any camera information, and downsampled our videos to match their resolution. As seen in Table 2, our method significantly outperforms VF in all metrics. This demonstrates the effectiveness of our mesh representation in producing 3D-consistent videos, in contrast to VF, which may learn geometric priors by training on a large-scale video dataset. Note that since VF does not follow a specific camera path, measuring its camera pose accuracy is not possible.

GEN-1 is conditioned on a source video, while our method takes camera trajectories as input. Thus, we used the RealEstate10K dataset [66], consisting of curated Internet videos and corresponding camera poses.

We automatically filtered 22 indoor videos that follow a smooth temporal backward camera motion to adapt it to our method's setting. The camera poses are used as input to our method, whereas the RGB frames are used as input video to GEN-1 while supplying both methods with the same prompt. We generate 110 videos 3 using five different prompts from our ten prompts set that allow reasonable editing of a house (such as 'library' rather than 'cave'). See Sec. B of appendix for more details.

We measure the video's 3D consistency metrics, as discussed in Sec. 4.2, and report them in Table 2. Our method outperforms GEN-1 in all metrics, even though the geometric structure of the video (depth maps) was given to GEN-1 as input.

4.3.2 Visual Quality and Scene Semantics

We conducted an extensive human perceptual evaluation as described in Sec. 4.1. We collect 3,000 user judgments over 60 video pairs from 50 participants. The results of this survey w.r.t. each method are reported in Table 2. As seen, when compared to VF, our method was preferred in 96% of the cases and in 69% of the cases in comparison with GEN-1. Sample results of those comparisons can be seen in Fig. 6. Note that since GEN-1, VF, and StableDiffusion do not exhibit the same underlying data distribution, existing visual quality metrics such as FID [17] cannot be used.

To measure how well our generated videos adhere to the desired scene, we measure the CLIP textsimilarity score - mean cosine similarity between the prompt and each of the frames. As seen in Table 2, our method follows the scene text prompt more closely than VF and is on par with GEN-1.

5 Discussion and Conclusion

We posed the task of text-driven 3D-consistent perpetual view generation and proposed a test-time optimization approach to generate long-term videos of diverse scenes. Our method demonstrates two key principles: (i) how to harness two powerful pre-trained models, allowing us to generate scenes in a zero-shot manner without requiring any training data from a specific domain, and (ii) how to combine these models with a unified 3D scene representation, which by construction ensures feasible geometry, and enables high quality and efficient rendering. As for limitations, the quality of our results depends on the generative and geometric priors and may sometime decrease over time due to error accumulation (Fig. 7 right). In addition, since we represent the scene with triangular meth, it is difficult for our method to represent dramatic depth discontinuities, e.g., sky vs. ground in outdoor scenes (Fig. 7 left). Moreover, since our method works in an online fashion and does not change the previously generated frames, the errors that appeared in previous frames can propagate to the next frames. Devising a new 3D scene representation tailored for the perpetual view generation of arbitrary scenes is an intriguing avenue of future research. We believe that the principles demonstrated in our work hold great promise in developing lightweight, 3D-aware video generation methods.

6 Acknowledgments

We thank Shai Bagon for his insightful comments. We thank Narek Tumanyan for his help with the website creation. We thank GEN-1 authors for their help in conducting comparisons. This project received funding from the Israeli Science Foundation (grant 2303/20).

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A Implementation Details

We provide implementation details for our framework and finetuning/generation regime.

Runtime. We use the Stable Diffusion [48] model that was additionally finetuned on the inpainted task to perform inpainting. We use DDIM scheduler [53] with 50 sampling steps for each generated frame. Synthesizing 50 frame-long videos with our full method takes approximately 2.5 hours on an NVIDIA TeslaV100 GPU. Specifically, Table 3 reports runtime required for each frame.

Depth prediction model and LDM decoder finetuning. We use MiDaS-DPT Large [42] as our depth prediction model. For each generated frame, we finetune it for 300 epochs, using Adam optimizer [25] with a learning rate of 1 e -7 . Additionally, we revert the weights of the depth prediction model to the initial state, as discussed in Sec. 3.3. We finetune the LDM decoder for 100 epochs on each generation step using Adam optimizer with a learning rate of 1 e -4 .

Camera path. Our camera follows a rotational motion combined with translation in the negative depth direction. Starting from a simple translation for k frames, every n frames we randomly sample a new rotation direction in the x -z plane (panning), that camera follows for n frames. In our experiments, we set k = 5 and n = 5 . We use PyTorch3D [44] to render and update our unified 3D representation.

Mask handling. We observed that the scene's geometry sometimes induces out-of-distribution inpainting masks for the Stable Diffusion inpainting model. To address this issue, we perform a morphological open operation on the inpainting mask: M O = Open ( M ) with kernel size 3. Then we inpaint the mask difference M -M O using Telea [55], while the inpainting model operates on M O afterward.

A.1 Mesh update.

As discussed in Sec. 3.5 in the paper, we update the mesh as follows: given an image I t +1 , a mask of pixels to unproject M , a corresponding depth map D t +1 and a camera pose C t +1 , we unproject the content in a process that is denoted in the main paper by ˜ M t +1 = UnProj ( M , I t +1 , D t +1 , C t +1 ) . First, each pixel center is unprojected by its depth value and camera pose into a 3D mesh vertex with the pixel's color as its vertex color. Then, each unprojected four neighboring pixels with coordinates ( i, j ) , ( i +1 , j ) , ( i, j +1) , ( i +1 , j +1) are used to define two adjacent triangle faces.

To prevent holes in the generated mesh we connect the existing mesh M t to the newly unprojected part ˜ M t +1 by inserting additional triangles. To do this, we first extract the boundary pixels surrounding the inpainting mask M , and find the faces from M t that are projected to the current view C t +1 . For each such face, we select the 3D point closest to the current camera center: p ∗ = arg min p ∈ ( p 1 ,p 2 ,p 3 ) ∥ p -c ∥ 2 , where c is the camera center of C t +1 , and ( p 1 , p 2 , p 3 ) are the points of a given triangle. Finally, we add the selected points to the triangulation scheme described above, which automatically creates the required triangles connecting M t and ˜ M t +1 .

(a) With antialiasing

A.2 Rendering

Antialiasing. Rendering an image from a mesh requires projecting points and faces to a 2D plane. The rasterization process often creates aliasing artifacts (Fig. 8, left), especially when a high-resolution mesh content from an earlier frame is rendered to a later frame, resulting in a large amount of triangle that needs to be rasterized into a low number of pixels. To avoid these artifacts, we apply antialiasing, similar to the image resize antialiasing method - we render the mesh at x2 higher resolution, apply Gaussian blur, and resize it to the required resolution.

Stretched triangles removal. As described in Sec. 3.6, the stretched triangles are forming between close and far away content along regions of depth discontinuities (Fig. 8, right), and we would like to remove them. Following Liu et al. [29], we apply the Sobel filter on the depth map D t to detect regions of depth discontinuities and threshold the values below the threshold of 0.3. Then we find triangles that are projected to the selected edge regions and filter them based on their normals. Specifically, we keep only the following triangles: { tr i | ( center ( tr i ) -c ) T n < ϵ } , where ϵ is the threshold, n is the normal of a triangle, and c is the camera center of C t +1 . In practice, we set ϵ = -0 . 05 .

Floating artifacts fix. As described in Sec 3.6, content at the border of the current frame can be mapped towards the interior of the next frame due to parallax. This creates "floating artifacts," shown in Fig. 3 in the paper. To overcome it, we pad the previous depth map D t with border depth values to 1.5x the rendering resolution, as if content exists beyond the image borders. Then, after we warp the padded depth to the next camera location, we get a new mask, M pad , that contains the content beyond the image borders. We use this mask to mask out the floating regions and thus enable the inpainting model to fill those holes with closer content. We perform this procedure on the image that was already rendered in 2x resolution due to the antialiasing step described above.

B Baseline Comparison Details

VideoFusion. Since VideoFusion [32] does not have an explicit way to control the motion presented in a video, we append 'zoom out video' and 'camera moving backward' to the input prompt to encourage the generated videos to follow a backward camera motion. We generate 1000 videos of 16 frames each, in 256 resolution. When comparing our method to theirs, we downsample our videos to 256 resolution. In this comparison, we use the full set of 10 prompts:

' indoor scene, interior, candy house, fantasy, beautiful, masterpiece, best quality '

' POV, haunted house, dark, wooden door, spider webs, skeletons '

' walkthrough, an opulent hotel with long, carpeted hallways, beautiful photo, masterpiece, indoor scene '

' A dimly lit library, with rows upon rows of leather-bound books and dark wooden shelves '

' walkthrough, inside a medieval castle, metal, beautiful photo, masterpiece, indoor scene '

' Simple museum, pictures, paintings, artistic, best quality, dimly lit '

' walkthrough, sci-fi ship interiors, corridors,amazing quality, masterpiece, beautiful scenery, best quality '

"Opulent hotel"

"Medieval castle"

"Candy house"

"Haunted house"

"Cave"

"Library"

' A grand, marble staircase spirals up to a vaulted ceiling in a grand entrance hall of a palace.A warm glow on the intricately designed floor '

' POV, cave, pools, water, dark cavern, inside a cave, beautiful scenery, best quality '

' inside a castle made of ice, beautiful photo, masterpiece '

GEN-1 To compare to GEN-1 [15], we used the RealEstate10K dataset [66], consisting of curated Internet videos and corresponding camera poses. We filtered 22 indoor videos that follow a smooth temporal backward camera motion to adapt it to our method's setting. To do that, we filter videos that adhere to the following constraints:

where c t is the center of the camera at frame t , and v t is the viewing direction (last column of the rotation matrix). We subsample the filtered videos to contain 25 frames and reverse the video if it had a positive average displacement ( c t +1 -c t ) in the z direction.

Since MiDaS produces depth maps that have a different range compared to the depth assumed in the RealEstate10K videos, we can't directly take the camera extrinsics from the RealEstate10K [66] data. To align those ranges, we need to find a scaling factor to multiply the MiDaS predictions with. To do that we run COLMAP and compute depth maps, using its dense reconstruction. Our scaling factor is then the ratio of median depth values of COLMAP and MiDaS:

where D C t and D t M are the COLMAP and MiDaS depth maps for frame t respectively, and N is the number of frames in the video.

For comparison to GEN-1 we used prompts #1-#5 from the above prompt list that allow reasonable editing of a video, depicting an interior of a house.

StableDreamFusion. In addition, we compare our method to StableDreamfusion [54], an opensource text-to-3D model capable of generating an implicit function of a scene (NeRF [35]) from text prompts. We provide it our camera trajectory and a simple text prompt such as ' a DSLR photo of the inside a cave ' (when providing prompts from our usual prompts set, StableDreamFusion was unable to converge to meaningful results). The generated scenes contain a lot of blur and unrealistic artifacts, as can be seen in Figure 9 since to achieve good visual quality, NERF requires multiple viewpoints of a scene from different angles. Full video results of StableDreamFusion can be found in the SM.

C Broader impact

Our framework is a test-time optimization method, which does not require any training data. Nevertheless, our framework uses two pre-trained models: a depth prediction model and a text-to-image diffusion model [48], which are susceptible to biases inherited to their training data (as discussed in Sec. 5 in [48]). In our context, these models are used to generate 3D static scenes. To avoid harmful content, we consider only text prompts that describe general scenery and objects, while avoiding prompts that involve sensitive content such as humans.

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Spiralcamerapath

"Pov, cave, pools, water, dark cavern, inside a cave, beautiful scehery, best quality, indoor scene"

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Spiralcamerapath

"ualkthrough, a medieval dungeon with damp, stone corridors and flickering torches lining the walls"

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Spiralcamerapath

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Bigturnscamerapath

"indoor scene, interior, candy house, fantasy, beautiful, masterpiece, best quality"

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Big turns camera path

"insideacastlemadeofice,beautifulphoto,masterpiece"

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Originalcamerapath

"inside a medieval forge, metal, fire"

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Originalcamerapath

"haunted house, dark, wooden door, spider webs, skeletons, indoor scehe"

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper present a method for text-driven perpetual view generation. It takes as input a text-prompt and carema trajectory as input, optimizing a unified mesh representation for rendering coherent video sequence accordingly. The key idea is straight-forward by connecting text-driven inpainting and depth warping with mesh-based representation to ensure 3D consistency.\n\nSTRENGTHS:\n- The paper proposes the first method for text-driven perpetual view generation with promising results, i.e., diverse scene textures and inter-frame coherence.\n- As an emerging direction with limited open-source work for comparisons, the authors have tried their best to compare the proposed method with others from different perspective, i.e., video generation, text-drive vid2vid, text-to-3d, for extensive evaluations.\n- The paper is well-organized and easy to follow.\n\nWEAKNESSES:\n- Only bounded indoor scene as the model is constrained by accuracy of depth map. However, outdoor unbounded scene is difficult to generate since depths has infinite ranges and discontinuities.\n- Moreover, the proposed method has the strong tendency to generate tunnel-specific scenes, i.e., a narrow hallway or tunnel through the space. It might be caused by the inpainting+depth continuity assumption. Because the surrounding walls acts as strong geometry cues for view consistency as camera moving backward.\n- Prompt engineering is required. Keywords like “POV” and “walkthrough” is critical for the generation results. The author should discuss their effects and design choices carefully as the paper claims using “free-vocabulary text”.\n- I may question the second contribution in the introduction, which claims the proposed method as the first zero-shot scene generation method. Unlike zero-shot text-to-3D object generation (DreamField, Dreamfusion, etc.) where a 3D representation is generated and camera can freely move, the proposed method binds the camera movement with the optimization process, which limits the camera trajectory after the optimization done. Therefore, it is only a 3D-aware video generation instead of 3D scene.\n- This is a personal one. The general idea (text-to-image + depth inpainting) of this method is not completely novel, with lots of engineering tricks to make it work. And I believe some details are missed to evaluate other technical parts like depth & decoder finetuning. Please check the Question section.\n- I believe the work miss some important literature for reference, in terms of zero-shot text-driven synthesis and indoor scene synthesis.\n    - zero-shot text-driven synthesis:\n        \n        For optimization-based one, there are StyleCLIP and etc. For training without text-image pair, there are LAFITE.\n        \n        StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery, Patashnik et al, ICCV 2021.\n        \n        LAFITE: Towards Language-Free Training for Text-to-Image Generation, Zhou et al, CVPR 2022.\n        \n    - scene synthesis / language-driven scene synthesis:\n        \n        Learning spatial knowledge for text to 3D scene generation, Chang et al, EMNLP 2014\n        \n        Language-driven synthesis of 3d scenes from scene databases, Ma et al, SIGGRAPH Asia 2018.\n        \n        Text2Light: Zero-shot Text-driven HDR Panorama Generation, Chen et al, SIGGRAPH Asia 2022.\n- typos:\n    - lacks a full stop in #L126\n    - Figure or Fig. is not consistent in the paper. For example, #L195.\n    - ‘mesh’ instead of ‘meth’ in #L285\n\nQUESTIONS:\n- Why finetune depth prediction model at test time? Finetuning may overfit/forgetting when the generated video is too long. Plus, the error would accumulate along with time steps. When I come to the ablation study of depth finetuning, I found that the RGB texture dose not jittering across video frame. In that case, does the depth map finetuning matter?\n- I’m not clear how depth finetuning really work as g is reverted at each time step.\n- The initialization of mesh M_0 is not clear to me. Does the number of point cloud is consistent with the nubmer of pixels? Moreover, how to get faces and edges of mesh from point cloud?\n- Online mesh update: the merging in Eq. 6 would lead to conflict. How to solve conflict is not introduced.\n- Figure 7 shows the failure cases resulted by error accumulation. Here comes the question, can authors evaluate the robustness and error rate of the method? For example, given a fixed set of text prompts, how many scenes can be successfully generated?\n- Moreover, I would like to see experiments or discussion of sensitivity to hyperparameters.\n\nLIMITATIONS:\nLimitations and negative societal impact are discussed.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2023-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper approaches the task of text-conditioned generation of a walkthrough video upon (mostly indoor) scenes. It proposes to leverage pretrained text-to-image and pretrained depth prediction models, to generate these videos without finetuning. Implementation generates videos in an online manner by repeated warping, inpainting and mesh aggregation. Contributions include test-time overfitting of depth and inpainting, and experiments show these are important to geometric  and semantic performance.\n\nSTRENGTHS:\nThis paper uses a cool insight to generate 3D walkthroughs using 2D pretrained models\n\n-\tThe paper leverages the power of large-pretrained models for online generation of many images in a consistent manner, without the need for finetuning. Careful implementation enables these images to create a consistent scene\n\n-\tVideos are long (50 frames with high movement), showing impressive consistency. They are also typically visually appealing (especially compared to a naïve implementation)\n\nSeveral contributions are executed to make performance high-quality\n\n-\tFinetuning depth and decoder modules with previously generated content meaningfully improves consistency\n\n-\tDealing with floating artifacts is a clear challenge that the proposed method deals with effectively (Fig 3)\n\n-\tThe method performs a union operation on mesh. This is not novel alone, but (correct) implementation is important to performance.\n\n-\tIn total, the proposed method improves COLMAP fitting errors by 3-6x over naïve method\n\nWEAKNESSES:\nThe method appears to be limited to backward movement, with little rotation. This trajectory setup makes the task easier and less interesting, as it does not handle side-to-side movement, or “looking around” which are important components of walkthroughs e.g. see RealEstate10K\n\n-\tConsider the motivation for this paper in L15-16 “By observing only a single photo of a scene, we can imagine what it would be like to explore it, move around, or turn our head and look around”. Unfortunately, this work does not answer this problem satisfactorily. Instead, it produces nice-looking and consistent results as we move backwards through an infinitely-long corridor, where we only slightly turn our head periodically.\n\n-\tMore “looking around” or freedom of movement would better test mesh is consistent across dramatically different views and does not have holes. As a viewer, it is also closer to the desired goal of exploring the generated scene.\n\n-\tIf the paper were instead focused on the infinite-generation direction, perhaps a more appropriate set of experiments and direction would be those relating to Infinite Nature?\n\nGenerated videos are of houses with unrealistic structure, breaking the desired illusion\n\n-\tThe trajectory of cameras means the video moves backward with a zig-zag type pattern. Walls tend to move correspondingly, and are typically equally at both edges of the video.\n\n-\tThis results in sequences where the viewer moves down an infinitely long hallway that goes back and forth as the camera does. This feels unrealistic; I can’t imagine an indoor structure that looks like any of the videos.\n\n-\tI feel prior work (e.g. GEN-1 or VideoFusion) actually may generate houses that are in some ways more realistic, as they do not always have walls at the edge of the video frame (although the difference in movement between each frame makes the comparison difficult to judge)\n\nExperiments feel somewhat ad-hoc. The small size and lack of consistency / justification for prompts make it harder to draw conclusions from results\n\n-\tAblations take place on 20 videos using 10 text prompts (small set). It is also surprising to use only 10 text prompts when generating 1000 videos for the VF experiment; or 5 text prompts on 110 videos against GEN-1. Results would be much more compelling if using a different prompt for each video. I’m curious if there is a limitation I am missing that could explain this.\n\n-\tPrompts are not programmatically generated, and I did not find a justification for the selected prompts (lack of consistency; do not reflect some true data distribution). Programmatic generation of prompts would also more easily enable scaling to larger sets.\n\n-\tPrompts contain significant unique detail e.g. “indoor scene, interior, candy house, fantasy, beautiful, masterpiece, best quality” and “POV, haunted house, dark, wooden door, spider webs, skeletons” (feels as if it could be prompt engineered, doesn’t feel reflective of some true distribution). \n\n\nEdit: Thanks for the rebuttal. I think the results with more significant turns are better, and showing some examples without prompt engineering is helpful.\n\nI feel after reading the rebuttal and other reviews the paper is still a 3 - reject. I stand by my original comment the lack of systematic experiments in the paper make it hard to draw clear scientific conclusions from this work. In additon, while results are consistent across frames, the completely unrealistic nature of generated houses mean this contribution is not impressive enough for acceptance in my mind.\n\nThe reviews of this paper are very mixed. Despite my negative review, accepting it would not be the end of the world if other reviewers end up feeling very positive given (1) the generated frame quality and cross-frame consistency and (2) a few technical contributions e.g. floaters, finetuning depth.\n\nQUESTIONS:\n-\tMotivating the intro with single-image novel view synthesis is a strange choice, and is a bit confusing, when the focus of this work is text-driven scene synthesis\n-\tMissing period L126\n-\tSlightly strange writing to see a list (L130) enumerated into subsections. It would feel more typical to change this sentence to finish with a period, and overview the subsections, or to simply make subsections as paragraphs.\n-\tOne addition metric that could be useful is CLIP R-Precision, which is used by prior work e.g. DreamFusion. By selecting some random frames in each video and ranking CLIP distance to all text prompts, one can compute precision. This could be helpful as automated visual semantic metrics can be limited in a setting such as this.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2023-06-26"}

comments: SUMMARY:
1. This paper proposes a method for test-driven consistent scene generation using a pretrained text-to-image model and depth prediction models.
2. The updating of mesh representation and fine-tuning of the decoder and depth prediction module enable continuous scene generation.

STRENGTHS:
1. The continuous scene generation demonstrated in both the main text and supplementary materials proves the effectiveness of the method. The proposed method generates more continuous results than the baseline method, given the same initialization and camera trajectory.
2. It is interesting to see the fancy effects achieved by utilizing pretrained inpainting and depth prediction models.

WEAKNESSES:
1. There is a lack of analysis on cumulative errors. 
2. The efficiency analysis provided in the supplementary materials could benefit from further optimization.

QUESTIONS:
1. The paper could provide additional analysis on cumulative errors and failure cases to further demonstrate the robustness of the proposed method.

LIMITATIONS:
The paper does address the limitations of the proposed method.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2023-07-06

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the authors study text-driven perpetual view synthesis with the input of text prompts describing the scene and desirable camera poses. Instead of training models on a specific domain, they propose an online scene generation method with a pretrained text-to-image model and a pretrained monocular depth prediction model. To further improve the video consistency, they utilize a shared mesh to represent the generated scene and deploy online time-time training to finetune the monocular depth prediction model for better mesh construction. The experiments demonstrate that the proposed method can generate geometrically-plausible scenes, and the proposed modules can improve the video quality quantitatively and qualitatively.\n\nSTRENGTHS:\n- The authors directly deploy a pretrained text-to-image model and a pretrained monocular depth prediction model as a strong indutive bias, enabling their method to work on diverse scenes in a zero-shot manner and eliminate training datasets.\n- The generated videos are consistent and geometrically plausible. With the text prompts, the users can decide on the generated scenes.\n- To further improve the online generated scenes, the authors deploy a unified mesh representation and adopt test-time training to finetune the depth prediction model, which are validated to improve the results in the extensive experiments.\n\nWEAKNESSES:\n- The main concern of the proposed method is that the presented results are nearly all with camera trajectory moving back. Can the proposed method work on trajectories similar to those in Text2Room? Or does some inherited property limit it to work on those camera trajectories?\n- I have a question on the need for the decoder finetuning. When we want to apply the text2image diffusion model for inpainting, we can replace the existing part with the input though the decoder can not perfectly reconstruct the input. In this case, why do we need decoder finetuning?\n- When watching the visual ablation study, it is actually hard to distinguish the improvement of the proposed modules. The demonstrated videos are nearly all moving backward and corridor scenarios. In this case, only the warp-inpaint baseline will work well.  \n- In Line 222, you mention the depth-aware splatting. Can you explain how you elaborate more on this baseline?\n- Why is the mesh so blurry in Figure 7 (left)? Worldsheet also uses the mesh representation, but their boundary is not that bad. Have you tried to split the mesh into foreground and background to help the discontinuity of the scene?\n- In the supplement video results, the warp-inpaint baseline is missed for visual ablation.\n\nQUESTIONS:\nPlease answer the questions mentions in the weakness part. I will adjust my score according to the rebuttal.\n\nLIMITATIONS:\nThe authors have properly addressed the limitation and broader impact in the main paper and supplement.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a text-driven perpetual view generation method that synthesizes long-term changing views of a scene and its corresponding 3D mesh, given the free-vocabulary text and camera trajectories. The authors apply an off-the-shelf pre-trained text-to-image diffusion model and a pre-trained depth prediction model to achieve the baseline quality of novel-view synthesis by reprojection and inpainting, and also apply a test-time finetuning scheme to enhance the geometry consistency and textural consistency throughout the view sequence.\n\nSTRENGTHS:\n1. The paper is easy to follow and provides many impressive qualitative results.\n2. It is promising that this method can synthesize diverse and zero-shot scenes without large-scale training data on a specific target domain. \n3. The SceneScape pipeline is technically sound to generate consistent views as its novel views are grounded in the scene geometry, where the generation of novel contents and geometrical structures can be readily adapted from off-the-shelf text-to-image diffusion-based inpainting/generation models and depth prediction model.\n3. By maintaining a unified 3D mesh representation and adopting a test-time finetuning technique, this method achieves even more consistent results. Such a technique is meaningful for camera pose-conditioned or other geometry-aware online scene generation pipelines.\n\nWEAKNESSES:\nI am almost satisfied with this manuscript. Here just list a few questions other than the limitations discussed in the paper, as follows:\n\n1. The SceneScape seems not to be able to handle dynamic foreground content. If the textual descriptions tell the model that it has to generate ``dancing people'', what will SceneScape do? Moreover, as one possible downstream application, how to edit the perpetual scenes generated by SceneScape?\n\n2. There are still quite a few strange distortions in the synthesized scenes. Can it be further improved by some instance-level priors? For example, knowing that there exists a chair, and we can use the prior knowledge of chairs to rectify the shape distortions? Maybe adding the prediction of instance segmentation maps into the SceneScape pipeline will be helpful.\n\nQUESTIONS:\n1. The footage provided in the supplementary is all about a walk-through towards a straight corridor. Curious about if it can handle more complex situations, e.g., turning left/right/back?\n\n2. How many prompts are used in total in the test stage? Can you give a full list of them? It is unclear if the prompts cover various indoor scenarios and further affect the metrics.\n\n3. It seems time-consuming to synthesize one frame according to the supplementary material. A comparison of the runtime with the reference methods would be helpful.\n\nLIMITATIONS:\nThe authors have stated limitations and potential negative societal impact in the paper.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2023-07-14"}