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- Joachim WeickertSuggested from co-authorship
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Works62 from public data
- 360
- Normalized Cut Loss for Weakly-Supervised CNN Segmentation351
This work proposes a new principled loss function evaluating network output with criteria standard in "shallow" segmentation, e.g. normalized cut which evaluates only seeds where labels are known while normalized cut softly evaluates consistency of all pixels.
- On Regularized Losses for Weakly-supervised CNN Segmentation339
This approach simplifies weakly-supervised training by avoiding extra MRF/CRF inference steps or layers explicitly generating full masks, while improving both the quality and efficiency of training.
- A Fully Progressive Approach to Single-Image Super-Resolution285
To obtain more photorealistic results, a generative adversarial network (GAN) is designed, named ProGanSR, that follows the same progressive multi-scale design principle and constitutes a principled multi- scale approach that increases the reconstruction quality for all upsampling factors simultaneously.
- 218
- PhaseNet for Video Frame Interpolation200
This work proposes a new approach, PhaseNet, that is designed to robustly handle challenging scenarios while also coping with larger motion, and shows that this is superior to the hand-crafted heuristics previously used in phase-based methods and compares favorably to recent deep learning based approaches for video frame interpolation on challenging datasets.
- High‐Resolution Neural Face Swapping for Visual Effects153
This is the first method capable of rendering photo‐realistic and temporally coherent results at megapixel resolution and incorporates a refinement strategy into the face landmark stabilization algorithm to achieve temporal stability, which is crucial for working with high‐resolution videos.
- 110
- Blind image super-resolution with spatially variant degradations98
This work proposes a solution that relies on a degradation aware SR network to synthesize the HR image given a low resolution image and the corresponding blur kernel and presents an optimization procedure that is able to recover both the degradation kernel and the high resolution image by minimizing the error predicted by the kernel discriminator.
- Cyclic Schemes for PDE-Based Image Analysis68
This work investigates a class of efficient numerical algorithms for many partial differential equations (PDEs) in image analysis that are applicable to parabolic or elliptic PDEs that have bounded coefficients and lead to space discretisations with symmetric matrices and shows that one should use parameter cycles that result from factorisations of box filters.
- Deep Generative Video Compression63
This work proposes an end-to-end, deep generative modeling approach to compress temporal sequences with a focus on video that builds upon variational autoencoder models for sequential data and combines them with recent work on neural image compression.
- VideoSnapping60
This work first derives a robust approximation of alignment quality between pairs of clips, computed as a weighted histogram of feature matches, and finds optimal temporal mappings (constituting frame correspondences) using a graph-based approach that allows for very efficient evaluation with artist constraints.
- 37
- Neural frame interpolation for rendered content34
This paper proposes a method specifically targeted to achieve high-quality frame interpolation for rendered content and is able to significantly push the state-of-the-art in frame interpolated motion estimates, more accurate occlusion handling and to correctly reconstruct non-linear motion between keyframes.
- Neural Video Compression with Spatio-Temporal Cross-Covariance Transformers33
This work proposes a new 3D-based transformer module: Spatio-Temporal Cross-Covariance Transformer (ST-XCT), which is integrated into various key coding components of NVC, such as feature extraction, frame reconstruction, and entropy modeling, demonstrating its generalizability.
- Controllable Inversion of Black-Box Face Recognition Models via Diffusion31
This method is the first black-box face recognition model inversion method that offers intuitive control over the generation process and leverages the stochastic nature of the denoising diffusion process to produce high-quality, identity-preserving face images with various backgrounds, lighting, poses, and expressions.
- An Omnistereoscopic Video Pipeline for Capture and Display of Real-World VR30
A first complete guide and analysis for reimplementing a system for capturing and displaying real-world VR video content, based on the concept of omnistereoscopic panoramas is provided.
- Anisotropic Range Image Integration27
This paper extends the state-of-the-art approach of Zach et al.(2007) in several ways, replacing the isotropic space-variant smoothing behaviour by an anisotropic (direction-dependent) one and using the more accurate closest signed distances instead of directional signed distances when converting range images into 3D signed distance fields.
- Deep Probabilistic Video Compression24
This model uses advances in variational autoencoders (VAEs) for sequential data and combines it with recent work on neural image compression to propose a variational inference approach to deep probabilistic video compression.
- 23
- Deep Adaptive Sampling and Reconstruction Using Analytic Distributions21
This work proposes an adaptive sampling and reconstruction method for offline Monte Carlo rendering that produces sampling maps constrained by a user-defined budget that minimize the expected future denoising error and supports joint end-to-end training of the sampling and Denoising networks.
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- Introducing Maximal Anisotropy into Second Order Coupling Models17
This work proposes a second order regulariser that allows to penalise both jumps and kinks in a direction-dependent way, and systematically introduces anisotropic concepts from first order approaches.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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