A podcast by researchers for researchers. This podcast aims to be a new medium for disseminating research. In each episode I talk to the main author of an academic paper in the field of computer vision, machine learning, artificial intelligence, graphics and everything in between. Each episode is structured like a paper and includes a TL;DR (abstract), related work, approach, results, conclusions and a future work section. It also includes the bonus "what did reviewer 2 say" section where authors share their experience in the peer review process. Enjoy!
All links are available in the blog post.
In this episode of the Talking Papers Podcast, I hosted Jiahao Zhang to chat about our CVPR 2023 paper "Aligning Step-by-Step Instructional Diagrams to Video Demonstrations".
furniture assembly diagram. To do that, we collected and annotated a brand new dataset: "IKEA Assembly in the Wild" where we aligned YouTube videos with IKEA's instruction man...
All links are available in the blog post: https://www.itzikbs.com/inr2vec/
In this episode of the Talking Papers Podcast, I hosted Luca De Luigi. We had a great chat about his paper “Deep Learning on Implicit Neural Representations of Shapes”, AKA INR2Vec, published in ICLR 2023 .
In this paper, they take implicit neural representations to the next level and use them as input signals for neural networks to solve m...
In this episode of the Talking Papers Podcast, I hosted Yael Vinker. We had a great chat about her paper "CLIPasso: SEmantically-Aware Object Sketching”, SIGGRAPH 2022 best paper award winner.
In this paper, they convert images into sketches with different levels of abstraction. They avoid the need for sketch datasets by using the well-known CLIP model to distil the semantic concepts from sketches and images. There is no netwo...
All links are available in the blog post.
In this episode of the Talking Papers Podcast, we hosted Amir Belder. We had a great chat about his paper "Random Walks for Adversarial Meshes”, published in SIGGRAPH 2022.
In this paper, they take on the task of creating an adversarial attack for triangle meshes. This is a non-trivial task since meshes are irregular. To solve the irregularity they use random walks ...
In this episode of the Talking Papers Podcast, I hosted Silvia Sellán. We had a great chat about her paper "Stochastic Poisson Surface Reconstruction”, published in SIGGRAPH Asia 2022.
In this paper, they take on the task of surface reconstruction with a probabilistic twist. They take the well-known Poisson Surface reconstruction algorithm and generalize it to give it a full statistical formalism. Essentially their method quan...
In this episode of the Talking Papers Podcast, I hosted Sameera Ranasinghe. We had a great chat about his paper "Beyond Periodicity: Towards a Unifying Framework for Activations in Coordinate-MLPs”, published in ECCV 2022 as an oral presentation.
In this paper, they propose a new family of activation functions for coordinate MLPs and provide a theoretical analysis of their effectiveness. Their main proposition is tha...
In this episode of the Talking Papers Podcast, I hosted Marko Mihajlovic . We had a great chat about his paper "KeypointNeRF: Generalizing Image-based Volumetric Avatars using Relative Spatial Encoding of Keypoints”, published in ECCV 2022.
In this paper, they create a generalizable NeRF for virtual avatars. To get a high-fidelity reconstruction of humans (from sparse observations), they leverage an off-the-shelf k...
In this episode of the Talking Papers Podcast, I hosted David B. Lindell to chat about his paper "BACON: Band-Limited Coordinate Networks for Multiscale Scene Representation”, published in CVPR 2022.
In this paper, they took on training a coordinate network. They do this by introducing a new type of neural network architecture that has an analytical Fourier spectrum. This allows them to do things like multi-scale s...
In this episode of the Talking Papers Podcast, I hosted Hsueh-Ti Derek Liu to chat about his paper "Learning Smooth Neural Functions via Lipschitz Regularization”, published in SIGGRAPH 2022.
In this paper, they took on the unique task of enforcing smoothness on Neural Fields (modelled as a neural network). They do this by introducing a regularization term that forces the Lipschitz constant of the network to be very small. The...
In this episode of the Talking Papers Podcast, I hosted Chamin Hewa Koneputugodage to chat about OUR paper "DiGS: Divergence guided shape implicit neural representation for unoriented point clouds”, published in CVPR 2022.
In this paper, we took on the task of surface reconstruction using a novel divergence-guided approach. Unlike previous methods, we do not use normal vectors for supervision. To compensate for that, we ...
In this episode of the Talking Papers Podcast, I hosted Dejan Azinović to chat about his paper "Neural RGB-D Surface Reconstruction”, published in CVPR 2022.
In this paper, they take on the task of RGBD surface reconstruction by using novel view synthesis. They incorporate depth measurements into the radiance field formulation by learning a neural network that stores a truncated signed distance field. This formulat...
In this episode of the Talking Papers Podcast, I hosted Yuliang Xiu to chat about his paper "ICON: Implicit Clothed humans Obtained from Normals”, published in CVPR 2022. SMPL(-X) body model to infer clothed humans (conditioned on the normals). Additionally, they propose an inference-time feedback loop that alternates between refining the body's normals and the shape.
"ICON: Implicit Cloth...
In this episode of the Talking Papers Podcast, I hosted Itai Lang to chat about his paper "SampleNet: Differentiable Point Cloud Sampling”, published in CVPR 2020. In this paper, they propose a point soft-projection to allow differentiating through the sampling operation and enable learning task-specific point sampling. Combined with their regularization and task-specific losses, they can reduce the number of points to 3% of t...
In this episode of the Talking Papers Podcast, I hosted Manuel Dahnert to chat about his paper “Panoptic 3D Scene Reconstruction From a Single RGB Image”, published in NeurIPS 2021. In this paper, they unify the task of reconstruction, semantic segmentation and instance segmentation in 3D from a single RGB image. They propose a holistic approach to lift the 2D features into a 3D grid. Manuel is a good friend and colleague. We fir...
In this episode of the Talking Papers Podcast, I hosted Songyou Peng to chat about his paper “Shape As Points: A Differentiable Poisson Solver”, published in NeurIPS 2021. In this paper, they take on the task of surface reconstruction and propose a hybrid representation that unifies explicit and implicit representation in addition to a differentiable solver for the classic Poisson surface reconstruction. I have been following Songy...
"VLN BERT: A Recurrent Vision-and-Language BERT for Navigation"
Yicong Hong, Qi Wu, Yuankai Qi, Cristian Rodriguez-Opazo, Stephen Gould
Accuracy of many visiolinguistic tasks has benefited significantly from the application of vision-and-language (V&L) BERT. However, its application for the task of vision and-language navigation (VLN) remains limited. O...
Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks
Despoina Paschalidou , Angelos Katharopoulos, Andreas Geiger, Sanja Fidler
Impressive progress in 3D shape extraction led to representations that can capture object geometries with high fidelity. In parallel, primitive-based methods seek to represent objects as semantically consistent part arrangement...
Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction
Guy Gafni Justus Thies Michael Zollhöfer Matthias Nießner
Project page: https://gafniguy.github.io/4D-Facial-Avatars/
We present dynamic neural radiance fields for modeling the appearance and dynamic...
"UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders"
Jing Zhang, Deng-Ping Fan, Yuchao Dai, Saeed Anwar, Fatemeh Sadat Saleh, Tong Zhang, Nick Barnes
In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D salie...
"Deep Declarative Networks: a new hope"
Stephen Gould, Richard Hartley, Dylan Campbell
We explore a new class of end-to-end learnable models wherein data processing nodes (or network layers) are defined in terms of desired behaviour rather than an explicit forward function. Specifically, the forward function is implicitly defined as the solution to a mathematic...
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In order to tell the story of a crime, you have to turn back time. Every season, Investigative journalist Delia D'Ambra digs deep into a mind-bending mystery with the hopes of reigniting interest in a decades old homicide case.
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