Recently, breakthroughs in video modeling have allowed for controllable camera trajectories in generated videos. However, these methods cannot be directly applied to user-provided videos that are not generated by a video model. In this paper, we present ReCapture, a method for generating new videos with novel camera trajectories from a single user-provided video. Our method allows us to re-generate the reference video, with all its existing scene motion, from vastly different angles and with cinematic camera motion. Notably, using our method we can also plausibly hallucinate parts of the scene that were not observable in the reference video. Our method works by (1) generating a noisy anchor video with a new camera trajectory using multiview diffusion models or depth-based point cloud rendering and then (2) regenerating the anchor video into a clean and temporally consistent reangled video using our proposed masked video fine-tuning technique.

Long exposure photography produces stunning imagery, representing moving elements in a scene with motion-blur. It is generally employed in two modalities, producing either a foreground or a background blur effect. Foreground blur images are traditionally captured on a tripod-mounted camera and portray blurred moving foreground elements, such as silky water or light trails, over a perfectly sharp background landscape. Background blur images, also called panning photography, are captured while the camera is tracking a moving subject, to produce an image of a sharp subject over a background blurred by relative motion. Both techniques are notoriously challenging and require additional equipment and advanced skills. In this paper, we describe a computational burst photography system that operates in a hand-held smartphone camera app, and achieves these effects fully automatically, at the tap of the shutter button. Our approach first detects and segments the salient subject. We track the scene motion over multiple frames and align the images in order to preserve desired sharpness and to produce aesthetically pleasing motion streaks. We capture an under-exposed burst and select the subset of input frames that will produce blur trails of controlled length, regardless of scene or camera motion velocity. We predict inter-frame motion and synthesize motion-blur to fill the temporal gaps between the input frames. Finally, we composite the blurred image with the sharp regular exposure to protect the sharpness of faces or areas of the scene that are barely moving, and produce a final high resolution and high dynamic range (HDR) photograph. Our system democratizes a capability previously reserved to professionals, and makes this creative style accessible to most casual photographers.

Taking photographs in low light using a mobile phone is challenging and rarely produces pleasing results. Aside from the physical limits imposed by read noise and photon shot noise, these cameras are typically handheld, have small apertures and sensors, use mass-produced analog electronics that cannot easily be cooled, and are commonly used to photograph subjects that move, like children and pets. In this paper we describe a system for capturing clean, sharp, colorful photographs in light as low as 0.3 lux, where human vision becomes monochromatic and indistinct. To permit handheld photography without flash illumination, we capture, align, and combine multiple frames. Our system employs "motion metering", which uses an estimate of motion magnitudes (whether due to handshake or moving objects) to identify the number of frames and the per-frame exposure times that together minimize both noise and motion blur in a captured burst. We combine these frames using robust alignment and merging techniques that are specialized for high-noise imagery. To ensure accurate colors in such low light, we employ a learning-based auto white balancing algorithm. To prevent the photographs from looking like they were shot in daylight, we use tone mapping techniques inspired by illusionistic painting: increasing contrast, crushing shadows to black, and surrounding the scene with darkness. All of these processes are performed using the limited computational resources of a mobile device. Our system can be used by novice photographers to produce shareable pictures in a few seconds based on a single shutter press, even in environments so dim that humans cannot see clearly.

Modeling human motion in complex environments without losing long-range dependencies is difficult due to the large number of combinatorially distinct paths humans may follow. Existing representations avoid this difficulty by limiting the prediction of human motion to a local level. As a result, robot motion planning algorithms that use these representations are reactive in nature, and fail to exploit higher-order dependencies. We present a novel motion model capable of representing the global path behavior of people. Our model compactly encodes higher-order temporal dependencies inherent in human mobility traces on an abstract representation of the environment that lends itself to combinatorial planning. We incorporate uncertainties into the planning process using POMDPs and present a general predictive multi-robot planning algorithm applicable to pedestrian datasets commonly found in the literature. We evaluate our planner by simulating multiple instances of a variant of the visibility-based target-tracking problem inspired by our previous work. We report encouraging results that demonstrate our multi-robot plans exhibit desirable combinatorial structure, e.g. robot re-use.

For battery-powered mobile robots to operate for long periods of time, it is critical to optimize their motion so as to minimize energy consumption. The driving motors are a major source of power consumption. In this paper, we study the problem of finding velocity profiles for car-like robots so as to minimize the energy consumed while traveling along a given path. We start with an established model for energy consumption of DC motors. We present closed form solutions for the unconstrained case and for the case where there is a bound on maximum velocity. We also study a general problem where the robot's path is composed of segments (e.g. circular arcs and line segments). We are given a velocity bound for each segment. For this problem, we present a dynamic programming solution which uses the solution for the single-constraint case as a subroutine. In addition, we present a calibration method to find model parameters. Finally, we present results from experiments conducted on a custom-built robot.

Telepresence or tele-immersion technologies allow people to attend a shared meeting without being physically present in the same location. Commercial telepresence solutions available in the market today have significant drawbacks - they are very expensive, and confine people to the area covered by stationary cameras. In this paper, we present a mobile tele-immersion platform that addresses these issues by using robots with embedded cameras. In our system, the users can move around freely because robots autonomously adjust their locations. We provide a geometric definition of what it means to get a good view of the user, and present control algorithms to maintain a good view. The algorithms are validated both in simulation and in real experiments.

Robotic routers (mobile robots with wireless communication capabilities) can create an adaptive wireless network and provide communication services for mobile users on-demand. Robotic routers are especially appealing for applications in which there is a single mobile user whose connectivity to a base station must be maintained in an environment that is large compared to the wireless range. In this paper, we study the problem of computing motion strategies for robotic routers in such scenarios, as well as the minimum number of robotic routers necessary to enact our motion strategies. Assuming that the routers are as fast as the user, we present an optimal solution for cases where the environment is a simply-connected polygon, a constant factor approximation for cases where the environment has a single obstacle, and an O(h) approximation for cases where the environment has h circular obstacles. The O(h) approximation also holds for cases where the environment has h arbitrary polygonal obstacles, provided they satisfy certain geometric constraints - e.g. when the set of their minimum bounding circles is disjoint.

In the lion and man game, a lion tries to capture a man who is as fast as the lion. We study a new version of this game which takes place in a Euclidean environment with a circular obstacle. We present a complete characterization of the game: for each player, we derive necessary and sufficient conditions for winning the game. Their (continuous time) strategies are constructed using techniques from differential games and arguments from geometry. Our main result is a decision algorithm which takes arbitrary initial positions as input, declares one of the players as the winner of the game and outputs a winning strategy for that player. We extend our approach to explicitly construct, in closed form, the decision boundary that partitions the arena into win and lose regions.

We study a variant of a well-known pursuit evasion game, the lion and man game. In this game a lion (the pursuer) tries to capture a man (the evader). The players move in turns. At each time step, they can move a unit distance. We focus on a version which takes place in an unbounded arena: the positive quadrant of the plane. The novelty of our formulation is in the sensor model. In the original formulation, the lion can sense the precise location of the man at all times. In our version, which is inspired by mobile robots equipped with monocular vision systems, the lion can only obtain bearing information about the man's location. We present a pursuit strategy which guarantees that the distance between the players is reduced to the step size in a bounded number of steps.