SlowGuess/NeurIPS_2023_OCR huggingface dataset:
where  $\sigma (\mathbf{x}_i,\tau_k)$  is the predicted sigma for point  $\mathbf{x}_i$ ,  $A(\mathbf{x}_i,\tau_k)$  is the predicted appearance feature. And  $\mathbf{f}_j, \mathbf{h}_j, \mathbf{g}_j$  and  $\mathbf{k}_j$  are vector-valued grid functions with output dimension  $M_{\sigma}$  or  $M_{app}$  respectively. The
final density value  $\sigma$  is the summation of the  $M_{\sigma}$  dimension density feature, and the final appearance feature is a weighted sum of the  $M_{app}$  dimension output feature, where  $\mathbf{B}$  is a simple linear layer. Note unlike HexPlane[6] and K-Planes[19], we only do grid interpolation to find the feature values for spatial coordinates, while we pick the time dimension according to the corresponding keyframe index. The final RGB color  $\mathbf{c}$  is regressed from a 2-layer MLP with appearance feature and view direction as inputs. With points' density  $\sigma$  and colors  $\mathbf{c}$ , images are rendered via volumetric rendering Equation (11).
In our implementation, for all the experiments, we set  $M_{\sigma}$  as 24 and  $M_{app}$  as 48, while for different experiments, we use different keyframe numbers. For object extrapolation task, we set keyframe number as 16, due to the more complex dynamics of the deformable objects. For both indoor extrapolation task and segmentation task, we set keyframe number as 4. The color decoder MLP has 2 hidden layers, each with 128 nodes.
A.3 Implementation Details of Velocity Field
The velocity field is defined as follows:
$$
\boldsymbol {v} = g _ {\Phi} (\boldsymbol {p}, t) = g _ {\Phi} (x, y, z, t) = w _ {\Phi} (x, y, z, t) \mathbf {M} _ {\mathbf {v}} (x, y, z) \quad \Phi \text {a r e t r a i n a b l e p a r a m e t e r s o f M L P s .} \tag {13}
$$
Here we decompose the velocity field as a weight MLP  $w_{\Phi}$  along with a velocity basis field. In our experiment, we find this can make the velocity convergence faster. The velocity basis field is defined as:
$$
\mathbf {M} _ {\mathbf {v}} (x, y, z) = \left[ \begin{array}{c c c} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \\ - z & 0 & x \\ - y & x & 0 \\ 0 & - z & y \end{array} \right]. \tag {14}
$$
The weight MLP is implemented as 4 hidden layers with 128 nodes, with a positional encoder of dim 3 as in [37]. The output is a weight vector  $\mathbf{w} \in \mathbb{R}^6$ , which could be regarded as the linear velocity in x, y, z directions and angular velocity rounding x, y, z axis respectively.
A.4 Additional Details of Physics Prior
We enforce the appearance and volume density features, denoted as  $f_{i}$  both admit the conservation laws characterized by the divergence-free implicit velocity field  $\mathbf{v}(\mathbf{x},t)$ :
$$
\frac {\partial f _ {i}}{\partial t} + \nabla \cdot (\mathbf {v} f _ {i}) = \frac {\partial f _ {i}}{\partial t} + \mathbf {v} \cdot \nabla f _ {i} + f _ {i} \nabla \cdot \mathbf {v} = \frac {D f _ {i}}{D t} + f _ {i} \nabla \cdot \mathbf {v} = 0, \quad \text {w h e r e} \frac {D}{D t} \text {i s t h e m a t e r i a l d e r i v a t i v e}.
$$
Intuitively, we assume the objects moved by the velocity field have a property of local rigidity, and the implicit velocity field should be incompressible (or in other words free of sources or sinks of mass). Then we can get the divergence-free constraint  $\nabla \cdot \mathbf{v} = 0$ .
The momentum of a velocity field is defined to be  $\rho \mathbf{v}$ , per unit volume. Newton's second law of motion states that momentum is conserved by a mechanical system of masses if no forces act on the system. If  $\mathbf{F}(\mathbf{x},t)$  is the force acting on the velocity, per unit volume, then we immediately have
$$
\rho \frac {D \mathbf {v} _ {i}}{D t} = \mathbf {F},
$$
where we assume the implicit velocity field has uniform mass density  $\rho$ . As a result, the momentum conservation constraint could be derived as:
$$
\frac {D \mathbf {v} _ {i}}{D t} = \frac {\partial \mathbf {v}}{\partial t} + \mathbf {v} \cdot \nabla \mathbf {v} = \frac {\mathbf {F}}{\rho} = \mathbf {a}. \tag {15}
$$
A.5 Implementation Details of Joint Optimization
During training, our keyframe radiance field starts with a space grid size of  $64^{3}$  and increases its resolution in log scale at 2k, 4k, 6k, 8k, 10k iterations till  $200^{3}$ . The learning rate for feature planes is 0.02, and the learning rate for color decoding neural network and velocity field is 0.001. All learning
rates are exponentially decayed to  $1 / 10$  at final iteration  $30\mathrm{k}$ . We use Adam[29] for optimization with  $\beta_{1} = 0.9, \beta_{2} = 0.99$ . We apply Total Variational loss [7, 6, 19] on all feature planes with  $\lambda = 0.001$  for spatial axes.
We sample 262144 points uniformly in the space  $[-1,1]^3$  and time  $[0,1]$  for dynamic object datasets, and 1310672 points for dynamic indoor scene datasets every iteration. The Jacobians of velocity required is calculated by using autograd from functorch[26]. For all the sampled points, we only evaluate the physics losses at occupied region, where the grid alpha  $\alpha = 1 - \exp -\sigma \times 0.01\geq 0.0001$ . We set the loss weight for divergence-free loss as 5, and the weight for momentum conservation loss as 0.1. All scenes are trained for 1.5 hours on a single NVIDIA RTX 3090 GPU respectively.
A.6 Additional Details of Datasets
Dynamic Object dataset: This dataset comprises 6 distinct objects<sup>1</sup>, displaying a variety of motion types, including both rigid and deformable movements. We use a total of 15 views, of which 12 are allocated for training and 3 for testing. Each view consists of 60 frames over 1 second; however, only the first 45 frames are used in the training set, with the remaining 15 frames for evaluation. Details of the 6 dynamic objects are:
- Falling Ball: A basketball is falling freely through a hoop. The basketball is accelerated by gravity, which should be learned by the model.
- Telescope: A telescope is given, whose upper part is rotating while the lower part remains static.
- Fan: An antique fan is given. The outer part of the fan is static, while the inner fan is rotating. The embedded motion makes this scene difficult.
- Bat: A bat is flapping its wings. Since the wing is extremely thin, and the motion is in a great extent, it's hard to reconstruct the motion.
- Shark: A shark is shaking its tail left and right.
- Whale: A whale is flapping its tail up and down.
Dynamic Indoor Scene dataset: This dataset consists of 4 indoor scenes, each containing several rigid objects  ${}^{2}$  undergoing different rigid body motions. Since the indoor scene is more challenging, it comprises 30 views, with 25 for training and 5 for testing. Like the first dataset, we use the first 45 frames from the 60-frame views for training and reserved the remaining frames for evaluation. A set of ground-truth segmentation masks is provided for evaluation. The segmentation mask is rendered by blender object index, which is perfectly accurate as ground truth. We have in total 4 scenes:
- Gnome House: 3 objects are moving outwards from the center of a house: a gnome, a sofa, and a treasure chest.
- Chessboard: 5 objects are moving towards each other on a chessboard: a horse statue, a marble bust, and a china vase from one side, a wooden elephant and a brass vase from the other side.
- Factory: 5 objects are moving towards nearly the same direction, among which 2 objects are rotating at the same time: three barrels with different appearance, a cardboard box, and a compost bag.
- Dining Table: 4 objects are falling down onto a dining table from the surrounding air: an apple, a cake, a croissant, and a lime.
A.7 Implementation Details about Semantic Scene Decomposition
NVFi(Ours): Figure 5 illustrates the optimization pipeline of our method for scene decomposition task:
$^{1}$ Basketball is downloaded from TurboSquid, licensed under the TurboSquid 3D Model License: https://blog.turbosquid.com/turbosquid-3d-model-license. Other objects are purchased from SketchFab, licensed under the SketchFab Standard License: https://sketchfab.com/licenses
2All objects and textures are freely downloaded from Poly Haven, licensed under Poly Haven asset License: https://polyhaven.com/licenses
Figure 5: The optimization pipeline of our method for scene decomposition. The solid black lines represent forward inference while the dashed gray lines represent backward supervision.
- First, we sample dense 3D points at timestamp  $t = 0$  and query their density values through the well-trained keyframe dynamic radiance field  $f_{\Theta}$ . By removing 3D points with low densities, we obtain a non-empty point set  $P$  with  $N$  points at timestamp  $t = 0$ , i.e.,  $P \in \mathbb{R}^{N \times 3}$ .
- Second, we feed the point set  $\pmb{P}$  into the to-be-trained scene decomposition network  $h_{\Psi}$  and obtain per-point object codes  $\pmb{O} \in (0,1)^{N \times K}$ , where  $K$  is a predefined number of objects that the network is expected to predict in maximum.
- Third, the points in  $\pmb{P}$  are transported to their correspondences  $\pmb{P}^{t'}$  using the well-trained velocity field  $g_{\Phi}$ . Thus, we can compute the per-point motion vectors  $\pmb{M}^{t'} \in \mathbb{R}^{N \times 3}$  as  $\pmb{M}^{t'} = \pmb{P}^{t'} - \pmb{P}$ .
- Lastly, we apply two losses proposed in OGC [54] onto the predicted object codes. 1) Dynamic rigid consistency: For the  $k^{th}$  object, We first retrieve its (soft) binary mask  $\pmb{O}^k$ , and feed the tuple  $\{\pmb{P},\pmb{P}^{\prime},\pmb{O}^{k}\}$  into the weighted-Kabsch algorithm to estimate its transformation matrix  $\pmb{T}_k\in \mathbb{R}^{4\times 4}$  belonging to  $SE(3)$  group. Then the dynamic loss is defined as:
$$
\ell_ {\text {d y n a m i c}} = \frac {1}{N} \sum_ {\boldsymbol {p} \in \boldsymbol {P}} \left\| \left(\sum_ {k = 1} ^ {K} o _ {\boldsymbol {p}} ^ {k} \cdot \left(\boldsymbol {T} _ {k} \circ \boldsymbol {p}\right)\right) - \left(\boldsymbol {p} + \boldsymbol {m} ^ {t ^ {\prime}}\right) \right\| _ {2} \tag {16}
$$
where  $o_{\pmb{p}}^{k}$  represents the probability of being assigned to the  $k^{th}$  object for a specific point  $\pmb{p}$ , and  $\pmb{m}^{t'} \in \mathbb{R}^3$  represents the motion vector of  $\pmb{p}$  from timestamp 0 to  $t'$ . The operation  $\circ$  applies the rigid transformation to the point. This loss serves to discriminate objects with different motions. 2) Spatial smoothness: We first search  $H$  nearest neighboring points for each point  $\pmb{p}$  in  $\pmb{P}$ . Then the smoothness loss is defined as:
$$
\ell_ {\text {s m o o t h}} = \frac {1}{N} \sum_ {\boldsymbol {p} \in P} \left(\frac {1}{H} \sum_ {h = 1} ^ {H} \| \boldsymbol {o} _ {\boldsymbol {p}} - \boldsymbol {o} _ {\boldsymbol {p} _ {h}} \| _ {1}\right) \tag {17}
$$
where  $\pmb{o}_{\pmb{p}} \in (0,1)^{K}$  represents the object assignment of center point  $\pmb{p}$ , and  $\pmb{o}_{\pmb{p}_h} \in (0,1)^{K}$  represents the object assignment of its  $h^{th}$  neighboring point. This loss servers to avoid the over-segmentation issues. We refer readers to [54] for more details.
For our scene decomposition network  $h_{\Psi}$ , we adopt a simple 4-layer MLP with 128 neurons in each hidden layer. The maximum number of predicted objects  $K$  is set to 8. A softmax activation is applied onto the predicted object code. During optimization, we adopt the Adam optimizer with a learning rate of 0.005 and optimize the network for 1000 iterations. The two losses  $\ell_{dynamic}$  and  $\ell_{smooth}$  are weighted by {1.0, 0.1}.
D-NeRF: For D-NeRF, we adopt exactly the same architecture of scene decomposition network as ours. The optimizer and other hyperparameters are also consistent with ours.
Mask2Former: We use the implementation of Mask2Former from MMDetection<sup>3</sup>, where we choose the most powerful Swin-L backbone. The model is pretrained on COCO dataset for panoptic segmentation task. Since we aim to decompose objects with different motions only, the ground plane and the wall in each scene are regarded as a whole static background, while the pre-trained model may separate them into different parts. For a fair comparison, we manually merge the segments in model's predictions corresponding to the static background.
Table 7: Quantitative results of ablation study for both interpolation task and extrapolation task on Dynamic Object dataset.
Joint	Physics	#K	Interpolation	Extrapolation
PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓
✓	✓	16	29.027	0.970	0.039	27.594	0.972	0.036
X	✓	16	27.525	0.959	0.057	24.792	0.955	0.059
✓	X	16	29.109	0.970	0.039	25.537	0.968	0.040
✓	✓	8	29.355	0.970	0.041	27.490	0.974	0.036
✓	✓	32	28.922	0.969	0.042	24.902	0.964	0.037
Table 8: Quantitative results of ablation study of the keyframe number on our Dynamic Indoor Scene dataset.
Number of Keyframes	Interpolation	Extrapolation
PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓
4	30.675	0.877	0.211	29.745	0.876	0.204
8	30.321	0.871	0.220	29.093	0.873	0.225
16	28.000	0.862	0.226	26.235	0.839	0.237
32	29.764	0.851	0.255	26.634	0.828	0.247
A.8 Additional Implementation Details about Motion Transfer
There are two models to train for this task. The first one is the velocity field model. The training detail of the velocity field is the same as future frame extrapolation task. In fact, we use the trained velocity from our future frame extrapolation task. The other model to train is a new static TensoRF [7] for the scene to be transferred. To be simple, we just set the keyframe number of our NVFi as 1, then we can get a static representation as TensoRF. The next step is to load the parameter from the pretrained velocity field to our new NVFi.
A.9 Additional Quantitative Results for Ablation Study
Here we show the total results for ablation study in Tables 7&8&9, both for interpolation and extrapolation.
A.10 Additional Quantitative Results for Future Frame Extrapolation
We report the quantitative results of interpolation and extrapolation tasks for individual objects/scenes in Dynamic Object dataset and Dynamic Indoor Scene dataset. As shown in Tables 10&11, our method achieves leading performance frequently on interpolation, and significantly outperforms all baselines on future frame extrapolation.
A.11 Additional Quantitative Results for Semantic Scene Decomposition
In the main paper, we report the excellent results of our model in Table 3 on semantic scene decomposition task based on a small integral time step 0.06. We also report the results with much larger integral time step 0.5. As shown in Tables 12&13, when we set the integral time step as 0.06, our segmentation results are significantly boosted, surpassing the two baselines by large margins.
A.12 Additional Qualitative results for Future Frame Extrapolation
We show the remaining qualitative results for interpolation and extrapolation tasks on Dynamic Objects Datasets in Figures 6&7, on Dynamic Indoor Scene Datasets in Figures 8&9. Images at time 0 and time 0.5 are within the observed time (interpolation), while time 1 is future frames. For each scene, the first three images are novel views, and the fourth one is from a seen viewing angle but at a future timestamp.
A.13 Additional Qualitative results for semantic scene decomposition
We show more qualitative results for semantic scene decomposition on Dynamic Indoor Scene dataset in Figures 10&11. Images at time 0 till time 0.67 are within observed time interval, while time 0.83 and time 1 is for future frames. Besides, we apply the semantic decomposition pipeline onto the Dynamic Object dataset for part decomposition. Qualitative results are shown in Figure 12.
Table 9: Quantitative results of ablation study of the camera number on our Dynamic Objects dataset.
Number of Cameras	Interpolation	Extrapolation
PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓
12	29.027	0.970	0.039	27.594	0.972	0.036
6	25.689	0.954	0.051	25.114	0.959	0.122
3	21.460	0.912	0.088	21.370	0.917	0.084
Table 10: Per-scene quantitative results of Dynamic Object dataset.
Methods	Falling Ball	Bat
Interpolation	Extrapolation	Interpolation	Extrapolation
PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓
T-NeRF[46]	14.921	0.782	0.326	15.418	0.793	0.308	13.070	0.836	0.234	13.897	0.834	0.230
D-NeRF[46]	15.548	0.665	0.435	15.116	0.644	0.427	14.087	0.845	0.212	15.406	0.887	0.175
TiNeuVox[18]	35.458	0.974	0.052	20.242	0.959	0.067	16.080	0.908	0.108	16.952	0.930	0.115
T-NeRFPINN	17.687	0.775	0.368	17.857	0.829	0.265	16.412	0.903	0.197	18.983	0.930	0.132
HexPlanePINN	32.144	0.965	0.065	20.762	0.951	0.081	23.399	0.958	0.057	21.144	0.951	0.064
NVFi(Ours)	35.826	0.978	0.041	31.369	0.978	0.041	23.325	0.964	0.046	25.015	0.968	0.042
Methods	Fan	Telescope
Interpolation	Extrapolation	Interpolation	Extrapolation
PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓
T-NeRF[46]	8.001	0.308	0.646	8.494	0.392	0.593	13.031	0.615	0.472	13.892	0.670	0.417
D-NeRF[46]	7.915	0.262	0.690	8.624	0.370	0.623	13.295	0.609	0.469	14.967	0.700	0.385
TiNeuVox[18]	24.088	0.930	0.104	20.932	0.935	0..078	31.666	0.982	0.041	20.456	0.921	0.067
T-NeRFPINN	9.233	0.541	0.508	9.828	0.606	0.443	14.293	0.739	0.366	15.752	0.804	0.298
HexPlanePINN	22.822	0.921	0.079	19.724	0.919	0.080	25.381	0.948	0.066	23.165	0.932	0.074
NVFi(Ours)	25.213	0.948	0.049	27.172	0.963	0.037	26.487	0.959	0.048	27.101	0.963	0.046
Methods	Shark	Whale
Interpolation	Extrapolation	Interpolation	Extrapolation
PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓
T-NeRF[46]	23.178	0.971	0.059	19.463	0.950	0.050	37.455	0.994	0.016	19.624	0.943	0.063
D-NeRF[46]	17.315	0.878	0.177	18.739	0.921	0.115	16.778	0.927	0.141	15.974	0.919	0.127
TiNeuVox[18]	28.874	0.976	0.040	22.330	0.961	0.047	29.634	0.981	0.035	21.391	0.961	0.053
T-NeRFPINN	32.072	0.984	0.024	28.874	0.982	0.021	31.240	0.986	0.025	26.032	0.978	0.029
A.14 Additional Qualitative Results for Motion Transfer
We have already shown results for Dynamic Indoor Scene datasets in Section 4.3. Here we report three results on objectwise motion transfer in Figure 13:
- Whale to starfish. We transfer the flapping tail motion to the starfish. The starfish is supposed to flapping its rays as the whale.
- Shark to whale. We transfer the shaking tail motion to the whale. The whale used to flap its tail, but now it's supposed to shake its tail.
- Whale to shark. We transfer the flapping tail motion to the shark. The shark is supposed to flap its tail as a whale instead of shaking it.
Table 11: Per-scene quantitative results of Dynamic Indoor Scene dataset.
Methods	Gnome House	Chessboard
Interpolation	Extrapolation	Interpolation	Extrapolation
PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓
T-NeRF[46]	26.094	0.716	0.383	23.485	0.643	0.419	25.517	0.796	0.294	20.228	0.708	0.365
D-NeRF[46]	27.000	0.745	0.319	21.714	0.641	0.367	24.852	0.774	0.308	19.455	0.675	0.384
TiNeuVox[18]	30.646	0.831	0.253	21.418	0.699	0.326	33.001	0.917	0.177	19.718	0.765	0.310
T-NeRFPINN	15.008	0.375	0.668	16.200	0.409	0.651	16.549	0.457	0.621	17.197	0.472	0.618
HexPlanePINN	23.764	0.658	0.510	22.867	0.658	0.510	24.605	0.778	0.412	21.518	0.748	0.428
NSFF[33]	31.418	0.821	0.294	25.892	0.750	0.327	32.514	0.810	0.201	21.501	0.805	0.282
NVFi(Ours)	30.667	0.824	0.277	30.408	0.826	0.273	30.394	0.888	0.215	27.840	0.872	0.219
Methods	Factory	Dining Table
Interpolation	Extrapolation	Interpolation	Extrapolation
PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓
T-NeRF[46]	16.634	0.446	0.624	17.546	0.480	0.609	16.807	0.486	0.640	18.215	0.548	0.595
D-NeRF[46]	27.200	0.826	0.283	24.998	0.792	0.312	25.291	0.788	0.350	22.979	0.771	0.355
TiNeuVox[18]	33.975	0.919	0.152	26.647	0.855	0.196	19.552	0.665	0.464	22.612	0.770	0.351
T-NeRFPINN	32.460	0.912	0.151	31.719	0.908	0.154	29.179	0.885	0.199	29.011	0.898	0.171
Table 12: Overall quantitative results for semantic scene decomposition.
AP↑	PQ↑	F1↑	Pre↑	Rec↑	mIoU↑
Mask2Former[11]	65.37	73.14	78.29	94.83	68.88	64.42
D-NeRF[46]	57.26	46.15	59.02	56.55	62.94	46.58
NVFi(Ours, Δt=0.5)	75.82	63.34	80.59	80.78	81.11	57.05
NVFi(Ours, Δt=0.06)	91.21	78.74	93.75	93.76	93.74	67.64
Table 13: Per-scene quantitative results for semantic scene decomposition.
Methods	Gnome House	Chessboard
AP↑	PQ↑	F1↑	Pre↑	Rec↑	mIoU↑	AP↑	PQ↑	F1↑	Pre↑	Rec↑	mIoU↑
Mask2Former[11]	60.89	73.05	77.32	85.32	70.69	66.94	82.68	81.35	90.81	97.54	84.94	76.17
D-NeRF[46]	80.54	62.24	85.28	85.28	85.28	54.82	57.12	48.11	60.22	56.20	64.85	48.97
NVFi(Ours, Δt=0.5)	99.00	82.99	99.92	99.92	99.92	66.42	48.30	43.14	61.19	61.53	60.84	45.95
NVFi(Ours, Δt=0.06)	100.00	85.01	100.00	100.00	100.00	68.01	67.97	57.95	76.96	76.96	74.96	56.79
Methods	Factory	Dining Table
AP↑	PQ↑	F1↑	Pre↑	Rec↑	mIoU↑	AP↑	PQ↑	F1↑	Pre↑	Rec↑	mIoU↑
Mask2Former[11]	40.25	53.54	57.60	99.01	40.61	37.76	77.65	84.61	87.42	97.44	79.28	76.80
D-NeRF[46]	17.33	17.08	21.29	25.35	18.35	20.72	74.05	57.15	69.30	59.35	83.27	61.82
NVFi(Ours, Δt=0.5)	64.82	57.75	77.93	85.72	71.44	49.70	91.17	69.49	83.31	75.96	92.23	66.13
NVFi(Ours, Δt=0.06)	98.86	80.17	99.09	99.09	99.09	69.07	98.01	91.81	98.95	98.99	98.92	76.68
Figure 6: Qualitative results of objects Fan and Telescope.
Figure 7: Qualitative results of objects Shark and Whale.
Figure 8: Qualitative results of scenes Gnome House and Chessboard.
Figure 9: Qualitative results of scenes Dining Table and Factory.
(a) Gnome House
(b) Chessboard
Figure 10: Qualitative results of semantic scene decomposition for Gnome House and Chessboard.
(c) Dining Table
(d) Factory
Figure 11: Qualitative results of semantic scene decomposition for Dining Table and Factory.
Figure 12: Qualitative results of semantic scene decomposition for Dynamic Object dataset.
Figure 13: Qualitative results for objectwise motion transfer.