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Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation
Nicolas Dufour, David Picard, Vicky Kalogeiton +1
Global visual geolocation predicts where an image was captured on Earth. Since images vary in how precisely they can be localized, this task inherently involves a significant degre…
PoM: Efficient Image and Video Generation with the Polynomial Mixer
David Picard, Nicolas Dufour
Diffusion models based on Multi-Head Attention (MHA) have become ubiquitous to generate high quality images and videos. However, encoding an image or a video as a sequence of patch…
E.T. the Exceptional Trajectories: Text-to-camera-trajectory generation with character awareness
Robin Courant, Nicolas Dufour, Xi Wang +2
Stories and emotions in movies emerge through the effect of well-thought-out directing decisions, in particular camera placement and movement over time. Crafting compelling camera…
Don't drop your samples! Coherence-aware training benefits Conditional diffusion
Nicolas Dufour, Victor Besnier, Vicky Kalogeiton +1
Conditional diffusion models are powerful generative models that can leverage various types of conditional information, such as class labels, segmentation masks, or text captions.…
OpenStreetView-5M: The Many Roads to Global Visual Geolocation
Guillaume Astruc, Nicolas Dufour, Ioannis Siglidis +10
Determining the location of an image anywhere on Earth is a complex visual task, which makes it particularly relevant for evaluating computer vision algorithms. Yet, the absence of…
Analysis of Classifier-Free Guidance Weight Schedulers
Xi Wang, Nicolas Dufour, Nefeli Andreou +4
Classifier-Free Guidance (CFG) enhances the quality and condition adherence of text-to-image diffusion models. It operates by combining the conditional and unconditional prediction…