SlowGuess/NeurIPS_2023_OCR huggingface dataset:
We compare against previous state-of-the-art (SoTA) methods for which open-source code is available. We have included PixelNeRF [69], a feature re-projection method, in our comparison. Additionally, we compare our methods against SparseFusion [76], the most recently published SoTA method that utilizes a diffusion model for NVs. We train our framework and SparseFusion on 10 categories of training sets. The PixelNeRF training was conducted per category due to its category-specific hyperparameters. For a fair comparison, all methods perform NVS without per-object optimization during the inference time. Because we do not replace the textual embedding in the pre-trained diffusion model, we use the prompt 'a picture of <class_name>' as the default prompt for both training and inference.
Figure 3: Novel view synthesizing results on open-set category objects with the same context image inputs, where SF denotes SparseFusion [76] and GT denotes Ground-Truth image. More results are given at our project webpage and appendix F.
Table 2: Quantitative evaluation metrics on 10 subset of training categories from CO3D, where PN denotes PixelNerf [69] and SF denotes SparseFusion [76]. Limited by the width, we only show FID and LPIPS score here.
Apple	Ball	Bench	Cake	Donut	Hydrant	Plant	Suitcase	Teddybear	Vase	Avg.
FID	LPIPS	FID	LPIPS	FID	LPIPS	FID	LPIPS	FID	LPIPS	FID	LPIPS	FID	LPIPS	FID	LPIPS	FID	LPIPS	FID	LPIPS	FID	LPIPS
PN	247.1	0.57	319.2	0.56	344.0	0.53	380.8	0.58	340.8	0.63	318.7	0.48	335.0	0.52	333.9	0.45	352.1	0.56	288.9	0.47	326.1	0.54
SF	110.9	0.28	143.5	0.30	255.8	0.34	185.7	0.33	126.6	0.29	165.7	0.23	168.5	0.31	202.6	0.28	199.3	0.34	167.8	0.23	172.6	0.29
Ours	42.8	0.19	45.8	0.19	122.5	0.25	105.2	0.23	67.2	0.21	87.8	0.16	86.2	0.23	100.2	0.20	91.2	0.23	69.1	0.16	81.8	0.21
4.3 Main Results Analysis
Given 2 context views, we evaluate novel view synthesis quality using the following metrics: FID [15], LPIPS [75], and PSNR  ${}^{2}$  . We believe that the combination of FID,LPIPS,and PSNR provides a comprehensive evaluation of novel view synthesis quality. FID and LPIPS measure the perceptual quality of the images, while PSNR measures the per-pixel accuracy. We note that PSNR has some drawbacks as a metric for evaluating generative models. Specifically, PSNR tends to favor blurry images that lack detail. This is because PSNR only measures the per-pixel accuracy of an image, and does not take into account the overall perceptual quality of the image. By using all three metrics, we can get a more complete picture of the quality of the images generated by our model.
4.3.1 Object Level Novel View Synthesis
In-Domain Evaluation We evaluate the performance of unseen objects NVS in training 10 categories. The quantitative results are presented in Table 5, which clearly demonstrates that our method surpasses the baseline methods in terms of both FID and LPIPS metrics. More specifically, DreamSparse outperforms SparseFusion by a substantial margin of  $53\%$  in the FID score and  $28\%$  in LPIPS. This significant improvement can be attributed to DreamSparse's capacity to generate sharper, semantically richer images, as depicted in Figure 1. This indicates the benefits of utilizing the potent image synthesis capabilities of pre-trained diffusion models.
Open-Set Evaluation We also evaluate the performance of objects NVS in open-set 10 categories, because PixelNerf is per-category trained, we do not report its open-set generalization results. According to Table 6, it is evident that our method surpasses the baseline in both evaluation metrics in all categories, surpassing the second-best method by  $28\%$  in LPIPS and  $43\%$  in FID. Moreover, the results derived from our method are not just competitive, but can even be compared favourably to the training category evaluations of the baseline in Table 5 (122.2 vs 172.2 in FID and 0.24 vs 0.29 in LPIPS). This clearly illustrates the benefits of utilizing 2D priors from a large-scale, pre-trained 2D diffusion model for open-set generalization. We also show the qualitative results in Figure 3, and it shows that the novel view image synthesised by our method can still achieve sharp and meaningful results on objects in open-set categories.
Table 3: Quantitative evaluation on 10 open-set categories outside of the training split, where SF denotes SparseFusion [76].
Bicycle	Car	Couch	Laptop	Microwave	Motorcycle	Bowl	Toyplane	TV	Wineglass	Avg.
FID	LPIPS	FID	LPIPS	FID	LPIPS	FID	LPIPS	FID	LPIPS	FID	LPIPS	FID	LPIPS	FID	LPIPS	FID	LPIPS	FID	LPIPS	FID
SF	217.7	0.34	209.5	0.30	201.1	0.44	223.8	0.36	200.4	0.40	205.8	0.35	198.4	0.26	202.2	0.26	261.4	0.33	208.5	0.23	212.9	0.33
Ours	154.7	0.26	116.5	0.22	129.9	0.31	167.3	0.26	127.2	0.29	115.4	0.29	61.9	0.16	98.5	0.19	167.9	0.24	82.7	0.15	122.2	0.24
Table 4: Quantitative evaluation metrics for object-centric scene-level novel view synthesis on the hydrant category from CO3D, where SF denotes SparseFusion [76].
1 View	2 Views	5 Views
FID↓	LPIPS↓	PSNR↑	FID↓	LPIPS↓	PSNR↑	FID↓	LPIPS↓	PSNR↑
PixelNeRF [69]	343.89	0.75	13.31	319.96	0.74	13.94	286.30	0.71	14.59
SparseFusion [76]	272.72	0.81	13.05	255.05	0.78	13.55	231.73	0.71	14.91
Ours	75.63	0.59	13.02	73.47	0.56	13.48	70.62	0.54	14.15
SparseFusion
PIXELNERF
PixelNERF
Figure 4: Qualitative results of Object-Centric Scene Level novel view synthesis outputs from all baselines.
4.3.2 Object-Centric Scene Novel View Synthesis
We report our evaluation results on object-centric scene-level NVs in Table 7. As shown in the table, DreamSparse significantly outperforms the baselines in terms of FID and LPIPS scores, surpassing the second-best performance by approximately  $70\%$  in FID and  $24\%$  in LPIPS, respectively. This underscores the effectiveness of our method in the context of object-centric scene NVs tasks. Despite our method showing comparable performance to the baseline in terms of Peak Signal-to-Noise Ratio (PSNR), it's worth mentioning that PSNR often favors blurry images lacking in detail [46, 44, 4]. This becomes evident in Figure 4, where despite our sharp and consistent synthesis results, PSNR still leans towards the blurry image produced by PixelNeRF.
4.4 Textual Control Style Transfer
As we do not replace or remove text conditioning in the pre-trained diffusion model, our method is additionally capable of controlling the image generation with text. We demonstrate an example use case where we conduct both novel view synthesis and style transfer via text in Figure 5.
$^{2}$ https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
+Photograph
+Snow
Figure 5: Qualitative results of novel view synthesis with textual control style transfer
+Oil Painting
+Monet
$\lambda = 0$
$\lambda = 0.5$
$\lambda = 1$
$\lambda = 2$
$\lambda = 5$
GT
Figure 6: Effect of the spatial guidance weighting of spatial guidance signals at the residual connections in the middle and decoder blocks of the diffusion model.
$\lambda = o$
$\lambda = 0.5$
$\lambda = 1$
$\lambda = 2$
$\lambda = 5$
GT
Context
2nd Block
3rd Block
4th Block
Output
GT
Figure 7: The spatial feature visualization with spatial guidance model, where context denotes the input context image, 2nd block denotes the visualization of feature maps from the 2nd block of the decoder and output denotes the synthesised novel view image.
Context
2nd Block
3rd Block
4th Block
Output
Number of Input Views We investigate the performance by varying the number of context view inputs. The results are depicted in Table 6, which clearly illustrates the enhancement in three evaluation metrics as the number of input views increases. Moreover, the performance disparity between the single view and multiple input views is less pronounced in our method than in other baselines - a  $6\%$  difference vs  $14\%$  difference in Sparse Fusion vs a  $17\%$  difference in PixelNerf. This observation leads to two key conclusions: 1) DreamSparse exhibits greater robustness in response to variations in the number of context view inputs. 2) Despite the decrease in performance, DreamSparse can efficiently synthesise novel views from a single input view.
Spatial Feature Visualization To investigate the impact of the spatial guidance model, we employ Principal Component Analysis (PCA) [35] to visualize the spatial features post-integration of spatial guidance following [63]. As shown in Figure 7, the visualized feature maps from the 2nd, 3rd, and 4th blocks of the UNet decoder indicate that despite the contextual view's geometry varying from that of the novel view, the feature maps steered by our spatial guidance model maintain alignment with the geometry of the ground truth image. This consistency enables the pre-trained diffusion model to generate images that accurately mirror the original geometry.
Spatial Guidance Weight We investigate the effects of spatial guidance weight on the quality and consistency of synthesized novel view images. Our study varies the spatial guidance weight  $\lambda$ , and the results in Fig 6 showed that when  $\lambda = 0$  (indicating no spatial guidance), the pre-trained diffusion model failed to synthesize a novel view image that was consistent in terms of geometry and identity. However, as the weight increased, the synthesized images exhibited greater consistency with the ground truth. It is important to note, though, that an excessively high weight could diminish the influence of features in the pre-trained diffusion model, potentially leading to blurry output. Given the trade-off between quality and consistency, we set  $\lambda = 2$  as the default hyperparameter.
Effect of Noise Perturbing Color Estimation The impact of the Noise Perturbation method is showcased in Figure 8. It is evident that when the diffusion process begins from random noise, the spatial guidance model can successfully guide the pre-trained diffusion model to synthesize images
Figure 8: Effect of noise perturbation level on novel view image synthesis. The images in the top row are visualizations of noised images as input to the diffusion process, and the images in the bottom row are the synthesized images from diffusion model. +5 noise denotes the addition of 5 steps of noise to the color estimate from the geometry model, and random noise denotes randomly sampled Gaussian noise. All images were generated using a DDIM [56] sampler with 20 inference steps.
with consistent geometry. However, the color or illumination density information is partially lost, leading to distortions in the synthesized novel view. In contrast, synthesizing the image from the noise that is added to the color estimation in the geometry model yields better results. As depicted in  $+20$  Noise' in Figure 8, the pre-trained diffusion model can effectively utilize the color information in the estimates, resulting in a more consistent image synthesis. We also experimented with varying the noise level added to the estimate. Our observations suggest that if the noise added to the blurry estimation is insufficient, the pre-trained diffusion model struggles to denoise the image because of the distribution mismatch between the blurry color estimate and Gaussian distribution, thereby failing to produce a sharp and consistent output.
In this paper, we present the DreamSparse, a framework that leverages the strong 2D priors of a frozen pre-trained text-to-image diffusion model for novel view synthesis from sparse views. Our method outperforms baselines on existing benchmarks in both training and open-set object-level novel view synthesis. Further results corroborate the benefits of utilizing a pre-trained diffusion model in object-centric scene NVS as well as in the generation of text-controlled scenes style transfer, clearly outperforming existing models and demonstrating the potential of leveraging the 2D pre-trained diffusion models for novel view synthesis.
Limitations and Negative Social Impact Despite its capabilities, we discovered that our 3D Geometry Module struggles with generating complex scenes, especially ones with non-standard geometry or intricate details. This is due to the limited capacity of the geometry module and limited data, and we will introduce a stronger geometry backbone and train it on larger datasets. On the social impact front, our technology could potentially lead to job displacement in certain sectors. For instance, professionals in fields such as graphic design or 3D modelling might find their skills becoming less in-demand as AI-based techniques become more prevalent and advanced. It's important to note that these negative implications are not exclusive to this study, and should be widely considered and addressed within the realm of AI research.
Computational resources of AI Bridging Cloud Infrastructure (ABCI) provided by National Institute of Advanced Industrial Science and Technology (AIST) were used for the experiments.
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Supplementary Material of “DreamSparse: Escaping from Plato’s Cave with 2D Diffusion Model Given Sparse Views”
Paul Yoo Jiaxian Guo† Yutaka Matsuo Shixiang Shane Gu The University of Tokyo {paulyoo, jiaxian.guo} @weblab.t.u-tokyo.ac.jp
To enhance visual presentation, we have showcased more qualitative samples on our paper's website. https://sites.google.com/view/dreamsparse-webpage. Next, we present the details of our method and additional quantitative comparisons with a recent baseline GBT and an ablation study on the 3D geometry prior module.
A Method Implementation Details
Feature Extraction Backbone. To encode a volumetric feature field, we input a context image through a ResNet50 [12] backbone and extract multi-scale feature maps from the first three blocks having feature dimensions 256, 512, and, 1024 respectively. To accommodate an additional depth dimension, the feature dimension is divided by the depth, which we set to 64, resulting in features of size 4, 8, 16 for the three volumes respectively. The final feature vector at a query 3D point is a concatenation of features from all three volume scales, making the final feature vector dimension 28.
Weighting Points Along a Ray. We employ a Transformer [64] to learn weightings for computing a linear combination of features of points along a given query ray. To form the input sequence for the Transformer, we follow the input parameterization of [57]. For each point, we concatenate the final feature vector from the backbone with an encoding of the query point and depth along the ray as a positional cue. The query point encoding is computed by first extracting relative poses for all context views with respect to the target view and representing the ray from each context camera origin to the query point in Plücker coordinates. The depth of each point along the ray is additionally parameterized by a sinusoidal positional encoding as in [29]. The output sequence of the Transformer is followed by a linear projection layer and a Softmax operation to yield scalar densities for computing a weighted sum of output features corresponding to points on a query ray.
Multi-view Aggregation Transformer. Once per-ray feature aggregate for each context view has been computed, we similarly learn to combine across all context views via a Transformer. We again concatenate per-ray feature vector output from the previous step with the same Plücker parameterization of query point and sinusoidal positional encoding of depth. All hidden and output dimensions of the Transformers are set to 256.
B Table Update with PSNR and an Additional Baseline
For completeness, we make an addition to the quantitative results to reflect PSNR measurements. Furthermore, we include another baseline GBT [65], a geometry-biased Transformer-based method for novel view synthesis from sparse context views, that demonstrates robust results on 10 categories of CO3D. Although GBT achieves comparable PSNR across training and open-set categories, synthesized novel views tend to be blurry especially for open-set categories and thus underperforming in FID and LPIPS.
*Correspondence to Jiaxian Guo <jiaxian.guo@weblab.t.u-tokyo.ac.jp>
$^{\ddagger}$ Correspondence to Jiaxian Guo <jiaxian.guo@weblab.t.u-tokyo.ac.jp>
Table 5: Quantitative evaluation metrics on 10 subset of training categories from CO3D, where PN denotes PixelNerf [69], SF denotes SparseFusion [76], and Geom Prior is the geometry prior module from our method.
Apple	Ball	Bench	Cake	Donut	Hydrant
FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑
PN	247.1	0.57	14.76	319.2	0.56	14.25	344.0	0.53	15.16	380.8	0.58	15.10	340.8	0.63	15.76	318.7	0.48	15.20
GBT	168.85	0.27	22.96	175.18	0.28	21.45	324.28	0.33	19.10	264.86	0.32	20.71	209.33	0.29	22.78	237.22	0.23	21.82
SF	110.9	0.28	20.91	143.5	0.30	20.25	255.8	0.34	17.21	185.7	0.33	19.39	126.6	0.29	20.62	165.7	0.23	19.35
Ours (Geom Prior)	123.98	0.33	22.96	134.41	0.33	22.09	238.95	0.38	19.42	197.00	0.35	21.41	162.12	0.34	22.95	211.03	0.26	21.90
Ours	42.8	0.19	23.72	45.8	0.19	23.12	122.5	0.25	18.68	105.2	0.23	21.73	67.2	0.21	22.88	87.8	0.16	21.71
Plant	Suitcase	Teddybear	Vase	Avg.
FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑
PN	335.0	0.52	18.08	333.9	0.45	20.12	352.1	0.56	14.85	288.9	0.47	15.91	326.1	0.54	15.91
GBT	254.74	0.29	21.29	283.38	0.28	23.41	294.84	0.32	19.93	255.22	0.26	22.28	246.79	0.29	21.57
SF	168.5	0.31	19.60	202.6	0.28	21.87	199.3	0.34	18.03	167.8	0.23	21.36	172.6	0.29	19.85
Ours (Geom Prior)	206.21	0.35	21.64	210.56	0.30	23.69	223.82	0.36	20.68	204.06	0.27	22.74	191.21	0.33	21.95
Ours	86.2	0.23	20.59	100.2	0.20	23.30	91.2	0.23	20.51	69.1	0.16	23.03	81.8	0.21	22.03
Table 6: Quantitative evaluation on 10 open-set categories outside of the training split, where SF denotes SparseFusion [76].
Bicycle	Car	Couch	Laptop	Microwave	Motorcycle
FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑
SF	217.7	0.34	17.57	209.5	0.30	17.49	201.1	0.44	19.25	223.8	0.36	18.73	200.4	0.40	17.65	205.8	0.35	17.52
GBT	259.77	0.33	19.10	287.64	0.33	18.18	272.82	0.43	20.10	276.15	0.36	19.75	276.44	0.41	17.40	294.84	0.35	18.50
Ours (Geom Prior)	235.36	0.38	19.39	218.86	0.34	19.11	182.56	0.39	21.58	231.58	0.39	21.09	213.55	0.45	19.26	259.89	0.41	18.65
Ours	154.7	0.26	18.16	116.5	0.22	18.38	129.9	0.31	20.36	167.3	0.26	20.13	127.2	0.29	18.96	115.4	0.29	17.35
Bowl	Toyplane	TV	Wineglass	Avg.
FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑	FID ↓	LPIPS ↓	PSNR ↑
SF	198.4	0.26	19.08	202.2	0.26	18.81	261.4	0.33	22.58	208.5	0.23	19.21	212.9	0.33	18.79
GBT	246.56	0.27	20.94	270.80	0.26	19.85	337.60	0.34	22.78	266.84	0.24	21.36	278.94	0.27	20.94
Ours (Geom Prior)	172.42	0.32	21.11	204.66	0.33	21.18	252.78	0.37	23.98	224.73	0.26	21.21	219.64	0.36	20.66
Ours	61.9	0.16	22.30	98.5	0.19	20.53	167.9	0.24	23.65	82.7	0.15	22.08	122.2	0.24	20.19
C Ablation Study for Geometry Model
We further evaluate novel view image estimates from the stand-alone geometry model alongside other baselines. From Tables 5, 6, 7, we observe the geometry model outperforming the full version of our method in terms of PSNR in many cases. This is a consequence of the geometry model often rendering blurry coarse images as the view deviates from the context images. As PSNR, being a measurement favoring lower mean squared error across pixels, encourages mean pixel color, blurry images tend to score higher. However, significant drops in FID and LPIPS indicate lower image quality and perceptual dissimilarity, highlighting the importance of the 2D prior module in imagining missing details.
Table 7: Quantitative evaluation metrics for object-centric scene novel view synthesis on the hydrant category from CO3D.
1 View	2 Views	5 Views
FID↓	LPIPS↓	PSNR↑	FID↓	LPIPS↓	PSNR↑	FID↓	LPIPS↓	PSNR↑
PixelNeRF [69]	343.89	0.75	13.31	319.96	0.74	13.94	286.30	0.71	14.59
SparseFusion [76]	272.72	0.81	13.05	255.05	0.78	13.55	231.73	0.71	14.91
Ours (Geom Prior)	322.46	0.70	14.53	279.16	0.65	15.37	216.34	0.57	17.16
Ours	75.63	0.59	13.02	73.47	0.56	13.48	70.62	0.54	14.15
D Evaluation Setup
For computing evaluation metrics, we select 10 objects per category and sample 32 uniformly spaced camera poses from the held-out test split. We then randomly select a specified number of context views from the camera poses and evaluate novel view synthesis results on the rest of the poses.
E Additional Training and Evaluation on ShapeNet