Kernkonzepte
Leveraging large-scale pretrained 2D diffusion models, we can efficiently generate diverse and coherent future video frames by conditioning on past context frames and their timestamps.
Zusammenfassung
The paper explores the task of forecasting future sensor observations given past observations. The authors are motivated by "predictive coding" concepts from neuroscience and applications in autonomous systems like self-driving vehicles.
The key insights are:
- Leveraging large-scale pretrained 2D diffusion models, which can handle multi-modality, by conditioning them on timestamps to build temporal understanding.
- Introducing invariances in the data by predicting modalities like grayscale or pseudo-depth, which simplifies the forecasting problem and allows efficient training on modest datasets.
The authors propose a video prediction diffusion network that conditions on past context frames and their timestamps. They explore different sampling schedules beyond the traditional autoregressive and hierarchical approaches, and find that their proposed "mixed" sampling performs the best.
Experiments on the TAO dataset show that the authors' method outperforms state-of-the-art video prediction baselines, especially in the long-horizon forecasting setting. They also find that predicting invariant modalities like depth or grayscale is easier than predicting RGB.
Statistiken
Predicting future depth maps is more accurate than predicting future RGB frames.
Directly jumping to a future frame performs better than autoregressive or hierarchical sampling strategies for long-horizon forecasting.
Conditioning on relative timestamps and randomizing the timestamp order during training helps the model learn better temporal understanding.
Zitate
"Our key insight is to leverage the large-scale pretraining of image diffusion models which can handle multi-modality."
"By introducing invariances in data and additionally learning to condition on frame timestamps, we are able to equip 2D diffusion models with the ability to perform predictive video modeling using moderately-sized training data."
"Motivated by probabilistic metrics from the object forecasting literature, we create a benchmark for video prediction on a diverse set of videos spanning indoor and outdoor scenes and a large vocabulary of objects."