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Found 18 papers, 9 papers with code
Benchmarking Video Frame Interpolation
Video frame interpolation, the task of synthesizing new frames in between two or more given ones, is becoming an increasingly popular research target.
Explorative Inbetweening of Time and Space
We introduce bounded generation as a generalized task to control video generation to synthesize arbitrary camera and subject motion based only on a given start and end frame.
Fast View Synthesis of Casual Videos
Specifically, we build a global static scene model using an extended plane-based scene representation to synthesize temporally coherent novel video.
Video Frame Interpolation with Many-to-many Splatting and Spatial Selective Refinement
In this work, we first propose a fully differentiable Many-to-Many (M2M) splatting framework to interpolate frames efficiently.
Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image
To do so, we propose a two-stage framework that first predicts depth up to an unknown scale and shift from a single monocular image, and then exploits 3D point cloud data to predict the depth shift and the camera's focal length that allow us to recover 3D scene shapes.
Towards Domain-agnostic Depth Completion
Our method leverages a data-driven prior in the form of a single image depth prediction network trained on large-scale datasets, the output of which is used as an input to our model.
Layered Depth Refinement with Mask Guidance
Depth maps are used in a wide range of applications from 3D rendering to 2D image effects such as Bokeh.
Many-to-many Splatting for Efficient Video Frame Interpolation
Motion-based video frame interpolation commonly relies on optical flow to warp pixels from the inputs to the desired interpolation instant.
Ranked #1 on
Video Frame Interpolation
on Xiph-4K (Crop)
Splatting-based Synthesis for Video Frame Interpolation
Frame interpolation is an essential video processing technique that adjusts the temporal resolution of an image sequence.
Learning to Recover 3D Scene Shape from a Single Image
Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length.
Ranked #1 on
Indoor Monocular Depth Estimation
on DIODE
(using extra training data)
Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes
We present a method to perform novel view and time synthesis of dynamic scenes, requiring only a monocular video with known camera poses as input.
Revisiting Adaptive Convolutions for Video Frame Interpolation
Video frame interpolation, the synthesis of novel views in time, is an increasingly popular research direction with many new papers further advancing the state of the art.
Learned Dual-View Reflection Removal
Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users.
Softmax Splatting for Video Frame Interpolation
In contrast, how to perform forward warping has seen less attention, partly due to additional challenges such as resolving the conflict of mapping multiple pixels to the same target location in a differentiable way.
Ranked #2 on
Video Frame Interpolation
on Middlebury
3D Ken Burns Effect from a Single Image
According to this depth estimate, our framework then maps the input image to a point cloud and synthesizes the resulting video frames by rendering the point cloud from the corresponding camera positions.
Ranked #4 on
Depth Estimation
on NYU-Depth V2
Context-aware Synthesis for Video Frame Interpolation
Finally, unlike common approaches that blend the pre-warped frames, our method feeds them and their context maps to a video frame synthesis neural network to produce the interpolated frame in a context-aware fashion.
Video Frame Interpolation via Adaptive Separable Convolution
Our method develops a deep fully convolutional neural network that takes two input frames and estimates pairs of 1D kernels for all pixels simultaneously.
Ranked #8 on
Video Frame Interpolation
on Middlebury
Video Frame Interpolation via Adaptive Convolution
Video frame interpolation typically involves two steps: motion estimation and pixel synthesis.
