We run AnyView on DROID, Kubric, and Ego-Exo4D below, and visualize camera trajectories on the right, showing that AnyView is capable of performing consistent extreme monocular dynamic view synthesis with high fidelity, even when the camera poses become very complex (2nd row).

While the AnyView generation does not match the ground truth precisely, it is still a plausible, realistic output that is coherent with the input. Existing methods tend to fail to extrapolate, largely copying the input view under large shifts in perspective. Meanwhile, our method preserves scene geometry, appearance, and dynamics, despite working with drastically different target poses and highly "incomplete" visual observations.

While the scenes below are not highly dynamic in terms of content, they do contain subtle, intricate motions and hand-object interactions, often with relatively small objects, as well as shaky input poses.

In videos similar to the examples below, the background often has to be “guessed” from the other camera viewpoint, but the inpainted regions integrate harmoniously with the rest of the scene.

We show anecdotal examples of AnyView leveraging subtle visuals cues to improve generation accuracy in unobserved areas, as evidence of advanced common sense and spatiotemporal reasoning. In the first example, the red car arriving at the intersection is predicted in the output (left) view before it is visible in the input (front) view, showing that AnyView has learned to maintain spatiotemporal consistency, leading to improved performance in areas that otherwise would be ill-defined.

Here, while the white van is partially visible in the first few frames, and thus gets depicted accurately in the remainder of the video, the white car is never observed in any input frame. Instead, AnyView appears to pick up on the headlights reflecting on the road. Although the reconstructed car does not have the correct appearance, the model indirectly estimates its trajectory by tracking the reflection over time.

Finally, the zero-shot ArgoVerse scene below depicts the ego vehicle pausing for a moment, and then turning left. The model correctly hallucinates the black car passing by on the left of the generated video, despite never observing it, presumably based on the suspicion that the driver must be waiting at the green light because of oncoming traffic before executing the unprotected left turn.

In the robotics video below, the model cannot see what is contained inside the black bin, because the contents are occluded, and resorts to predicting fruit (since those objects are common in LBM), in addition to spawning spurious objects out-of-frame on the left. Video layout:

Next, in Kubric, we often observe variations of object positions along the input viewing direction, which presumably stems primarily from uncertainty in terms of "implicit" depth estimation by the model.

While these target viewpoints only exist in synthetic training data, almost every car that AnyView can see is reconstructed with high fidelity and accurate dynamics.