VTinker: Guided Flow Upsampling and Texture Mapping for High-Resolution Video Frame Interpolation Article Swipe
Due to large pixel movement and high computational cost, estimating the motion of high-resolution frames is challenging. Thus, most flow-based Video Frame Interpolation (VFI) methods first predict bidirectional flows at low resolution and then use high-magnification upsampling (e.g., bilinear) to obtain the high-resolution ones. However, this kind of upsampling strategy may cause blur or mosaic at the flows' edges. Additionally, the motion of fine pixels at high resolution cannot be adequately captured in motion estimation at low resolution, which leads to the misalignment of task-oriented flows. With such inaccurate flows, input frames are warped and combined pixel-by-pixel, resulting in ghosting and discontinuities in the interpolated frame. In this study, we propose a novel VFI pipeline, VTinker, which consists of two core components: guided flow upsampling (GFU) and Texture Mapping. After motion estimation at low resolution, GFU introduces input frames as guidance to alleviate the blurring details in bilinear upsampling flows, which makes flows' edges clearer. Subsequently, to avoid pixel-level ghosting and discontinuities, Texture Mapping generates an initial interpolated frame, referred to as the intermediate proxy. The proxy serves as a cue for selecting clear texture blocks from the input frames, which are then mapped onto the proxy to facilitate producing the final interpolated frame via a reconstruction module. Extensive experiments demonstrate that VTinker achieves state-of-the-art performance in VFI. Codes are available at: https://github.com/Wucy0519/VTinker.
Related Topics
Concepts
Upsampling
Computer vision
Ghosting
Artificial intelligence
Bilinear interpolation
Computer science
Motion interpolation
Motion estimation
Interpolation (computer graphics)
Optical flow
Motion compensation
Pixel
Bicubic interpolation
Classification of discontinuities
Texture mapping
Reference frame
Stairstep interpolation
Mathematics
Motion blur
Nearest-neighbor interpolation
Image texture
Image resolution
Frame (networking)
Frame rate
Inter frame
Metadata
- Type
- article
- Landing Page
- http://arxiv.org/abs/2511.16124
- https://arxiv.org/pdf/2511.16124
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7106476301
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W7106476301Canonical identifier for this work in OpenAlex
- Title
-
VTinker: Guided Flow Upsampling and Texture Mapping for High-Resolution Video Frame InterpolationWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-20Full publication date if available
- Authors
-
Wu Chenyang, Fu Jiayi, Guo, Chun-Le, Han, Shuhao, Li, ChongyiList of authors in order
- Landing page
-
https://arxiv.org/abs/2511.16124Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2511.16124Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2511.16124Direct OA link when available
- Concepts
-
Upsampling, Computer vision, Ghosting, Artificial intelligence, Bilinear interpolation, Computer science, Motion interpolation, Motion estimation, Interpolation (computer graphics), Optical flow, Motion compensation, Pixel, Bicubic interpolation, Classification of discontinuities, Texture mapping, Reference frame, Stairstep interpolation, Mathematics, Motion blur, Nearest-neighbor interpolation, Image texture, Image resolution, Frame (networking), Frame rate, Inter frameTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
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| abstract_inverted_index.initial | 166 |
| abstract_inverted_index.methods | 24 |
| abstract_inverted_index.module. | 207 |
| abstract_inverted_index.predict | 26 |
| abstract_inverted_index.propose | 110 |
| abstract_inverted_index.texture | 184 |
| abstract_inverted_index.However, | 44 |
| abstract_inverted_index.Mapping. | 128 |
| abstract_inverted_index.VTinker, | 115 |
| abstract_inverted_index.achieves | 213 |
| abstract_inverted_index.bilinear | 147 |
| abstract_inverted_index.blurring | 144 |
| abstract_inverted_index.captured | 71 |
| abstract_inverted_index.clearer. | 154 |
| abstract_inverted_index.combined | 95 |
| abstract_inverted_index.consists | 117 |
| abstract_inverted_index.ghosting | 99, 159 |
| abstract_inverted_index.guidance | 140 |
| abstract_inverted_index.movement | 4 |
| abstract_inverted_index.referred | 169 |
| abstract_inverted_index.strategy | 49 |
| abstract_inverted_index.Extensive | 208 |
| abstract_inverted_index.alleviate | 142 |
| abstract_inverted_index.available | 220 |
| abstract_inverted_index.bilinear) | 38 |
| abstract_inverted_index.generates | 164 |
| abstract_inverted_index.pipeline, | 114 |
| abstract_inverted_index.producing | 199 |
| abstract_inverted_index.resulting | 97 |
| abstract_inverted_index.selecting | 182 |
| abstract_inverted_index.adequately | 70 |
| abstract_inverted_index.estimating | 9 |
| abstract_inverted_index.estimation | 74, 131 |
| abstract_inverted_index.facilitate | 198 |
| abstract_inverted_index.flow-based | 19 |
| abstract_inverted_index.inaccurate | 88 |
| abstract_inverted_index.introduces | 136 |
| abstract_inverted_index.resolution | 31, 67 |
| abstract_inverted_index.upsampling | 36, 48, 124, 148 |
| abstract_inverted_index.components: | 121 |
| abstract_inverted_index.demonstrate | 210 |
| abstract_inverted_index.experiments | 209 |
| abstract_inverted_index.performance | 215 |
| abstract_inverted_index.pixel-level | 158 |
| abstract_inverted_index.resolution, | 77, 134 |
| abstract_inverted_index.challenging. | 16 |
| abstract_inverted_index.intermediate | 173 |
| abstract_inverted_index.interpolated | 104, 167, 202 |
| abstract_inverted_index.misalignment | 82 |
| abstract_inverted_index.Additionally, | 59 |
| abstract_inverted_index.Interpolation | 22 |
| abstract_inverted_index.Subsequently, | 155 |
| abstract_inverted_index.bidirectional | 27 |
| abstract_inverted_index.computational | 7 |
| abstract_inverted_index.task-oriented | 84 |
| abstract_inverted_index.reconstruction | 206 |
| abstract_inverted_index.discontinuities | 101 |
| abstract_inverted_index.high-resolution | 13, 42 |
| abstract_inverted_index.pixel-by-pixel, | 96 |
| abstract_inverted_index.discontinuities, | 161 |
| abstract_inverted_index.state-of-the-art | 214 |
| abstract_inverted_index.high-magnification | 35 |
| abstract_inverted_index.https://github.com/Wucy0519/VTinker. | 222 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.8399408 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |