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Found 6 papers, 0 papers with code
Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous Scenes
We present a method enabling the scaling of NeRFs to learn a large number of semantically-similar scenes.
DiVa-360: The Dynamic Visual Dataset for Immersive Neural Fields
Advances in neural fields are enabling high-fidelity capture of the shape and appearance of dynamic 3D scenes.
STDepthFormer: Predicting Spatio-temporal Depth from Video with a Self-supervised Transformer Model
Apart from the transformer architecture, one of the main contributions with respect to prior works lies in the objective function that enforces spatio-temporal consistency across a sequence of output frames rather than a single output frame.
Forecasting of depth and ego-motion with transformers and self-supervision
This paper addresses the problem of end-to-end self-supervised forecasting of depth and ego motion.
Instance-aware multi-object self-supervision for monocular depth prediction
One novelty of the proposed method is the use of the multi-head attention of the transformer network that matches moving objects across time and models their interaction and dynamics.
Are conditional GANs explicitly conditional?
This paper proposes two important contributions for conditional Generative Adversarial Networks (cGANs) to improve the wide variety of applications that exploit this architecture.
