I2VControl-Camera: Precise Video Camera Control with Adjustable Motion Strength

Video generation technologies are developing rapidly and have broad potential applications. Among these technologies, camera control is crucial for generating professional-quality videos that accurately meet user expectations. However, existing camera control methods still suffer from several limitations, including control precision and the neglect of the control for subject motion dynamics. In this work, we propose I2VControl-Camera, a novel camera control method that significantly enhances controllability while providing adjustability over the strength of subject motion. To improve control precision, we employ point trajectory in the camera coordinate system instead of only extrinsic matrix information as our control signal. To accurately control and adjust the strength of subject motion, we explicitly model the higher-order components of the video trajectory expansion, not merely the linear terms, and design an operator that effectively represents the motion strength. We use an adapter architecture that is independent of the base model structure. Experiments on static and dynamic scenes show that our framework outperformances previous methods both quantitatively and qualitatively. The project page is: https://wanquanf.github.io/I2VControlCamera .

I2VControl: Disentangled and Unified Video Motion Synthesis Control

Video synthesis techniques are undergoing rapid progress, with controllability being a significant aspect of practical usability for end-users. Although text condition is an effective way to guide video synthesis, capturing the correct joint distribution between text descriptions and video motion remains a substantial challenge. In this paper, we present a disentangled and unified framework, namely I2VControl, that unifies multiple motion control tasks in image-to-video synthesis. Our approach partitions the video into individual motion units and represents each unit with disentangled control signals, which allows for various control types to be flexibly combined within our single system. Furthermore, our methodology seamlessly integrates as a plug-in for pre-trained models and remains agnostic to specific model architectures. We conduct extensive experiments, achieving excellent performance on various control tasks, and our method further facilitates user-driven creative combinations, enhancing innovation and creativity. The project page is: https://wanquanf.github.io/I2VControl .

Global Censored Quantile Random Forest

In recent years, censored quantile regression has enjoyed an increasing popularity for survival analysis while many existing works rely on linearity assumptions. In this work, we propose a Global Censored Quantile Random Forest (GCQRF) for predicting a conditional quantile process on data subject to right censoring, a forest-based flexible, competitive method able to capture complex nonlinear relationships. Taking into account the randomness in trees and connecting the proposed method to a randomized incomplete infinite degree U-process (IDUP), we quantify the prediction process' variation without assuming an infinite forest and establish its weak convergence. Moreover, feature importance ranking measures based on out-of-sample predictive accuracy are proposed. We demonstrate the superior predictive accuracy of the proposed method over a number of existing alternatives and illustrate the use of the proposed importance ranking measures on both simulated and real data.

WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents

Can large language models (LLMs) directly serve as powerful world models for model-based agents? While the gaps between the prior knowledge of LLMs and the specified environment's dynamics do exist, our study reveals that the gaps can be bridged by aligning an LLM with its deployed environment and such "world alignment" can be efficiently achieved by rule learning on LLMs. Given the rich prior knowledge of LLMs, only a few additional rules suffice to align LLM predictions with the specified environment dynamics. To this end, we propose a neurosymbolic approach to learn these rules gradient-free through LLMs, by inducing, updating, and pruning rules based on comparisons of agent-explored trajectories and world model predictions. The resulting world model is composed of the LLM and the learned rules. Our embodied LLM agent "WALL-E" is built upon model-predictive control (MPC). By optimizing look-ahead actions based on the precise world model, MPC significantly improves exploration and learning efficiency. Compared to existing LLM agents, WALL-E's reasoning only requires a few principal rules rather than verbose buffered trajectories being included in the LLM input. On open-world challenges in Minecraft and ALFWorld, WALL-E achieves higher success rates than existing methods, with lower costs on replanning time and the number of tokens used for reasoning. In Minecraft, WALL-E exceeds baselines by 15-30% in success rate while costing 8-20 fewer replanning rounds and only 60-80% of tokens. In ALFWorld, its success rate surges to a new record high of 95% only after 6 iterations.

Body Fat Estimation from Surface Meshes using Graph Neural Networks

Body fat volume and distribution can be a strong indication for a person's overall health and the risk for developing diseases like type 2 diabetes and cardiovascular diseases. Frequently used measures for fat estimation are the body mass index (BMI), waist circumference, or the waist-hip-ratio. However, those are rather imprecise measures that do not allow for a discrimination between different types of fat or between fat and muscle tissue. The estimation of visceral (VAT) and abdominal subcutaneous (ASAT) adipose tissue volume has shown to be a more accurate measure for named risk factors. In this work, we show that triangulated body surface meshes can be used to accurately predict VAT and ASAT volumes using graph neural networks. Our methods achieve high performance while reducing training time and required resources compared to state-of-the-art convolutional neural networks in this area. We furthermore envision this method to be applicable to cheaper and easily accessible medical surface scans instead of expensive medical images.

Certifiably-correct Control Policies for Safe Learning and Adaptation in Assistive Robotics

Guaranteeing safety in human-centric applications is critical in robot learning as the learned policies may demonstrate unsafe behaviors in formerly unseen scenarios. We present a framework to locally repair an erroneous policy network to satisfy a set of formal safety constraints using Mixed Integer Quadratic Programming (MIQP). Our MIQP formulation explicitly imposes the safety constraints to the learned policy while minimizing the original loss function. The policy network is then verified to be locally safe. We demonstrate the application of our framework to derive safe policies for a robotic lower-leg prosthesis.

Safe Robot Learning in Assistive Devices through Neural Network Repair

Assistive robotic devices are a particularly promising field of application for neural networks (NN) due to the need for personalization and hard-to-model human-machine interaction dynamics. However, NN based estimators and controllers may produce potentially unsafe outputs over previously unseen data points. In this paper, we introduce an algorithm for updating NN control policies to satisfy a given set of formal safety constraints, while also optimizing the original loss function. Given a set of mixed-integer linear constraints, we define the NN repair problem as a Mixed Integer Quadratic Program (MIQP). In extensive experiments, we demonstrate the efficacy of our repair method in generating safe policies for a lower-leg prosthesis.

Local Repair of Neural Networks Using Optimization

In this paper, we propose a framework to repair a pre-trained feed-forward neural network (NN) to satisfy a set of properties. We formulate the properties as a set of predicates that impose constraints on the output of NN over the target input domain. We define the NN repair problem as a Mixed Integer Quadratic Program (MIQP) to adjust the weights of a single layer subject to the given predicates while minimizing the original loss function over the original training domain. We demonstrate the application of our framework in bounding an affine transformation, correcting an erroneous NN in classification, and bounding the inputs of a NN controller.

Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest

Due to their long-standing reputation as excellent off-the-shelf predictors, random forests continue remain a go-to model of choice for applied statisticians and data scientists. Despite their widespread use, however, until recently, little was known about their inner-workings and about which aspects of the procedure were driving their success. Very recently, two competing hypotheses have emerged -- one based on interpolation and the other based on regularization. This work argues in favor of the latter by utilizing the regularization framework to reexamine the decades-old question of whether individual trees in an ensemble ought to be pruned. Despite the fact that default constructions of random forests use near full depth trees in most popular software packages, here we provide strong evidence that tree depth should be seen as a natural form of regularization across the entire procedure. In particular, our work suggests that random forests with shallow trees are advantageous when the signal-to-noise ratio in the data is low. In building up this argument, we also critique the newly popular notion of "double descent" in random forests by drawing parallels to U-statistics and arguing that the noticeable jumps in random forest accuracy are the result of simple averaging rather than interpolation.

Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance

As the size, complexity, and availability of data continues to grow, scientists are increasingly relying upon black-box learning algorithms that can often provide accurate predictions with minimal a priori model specifications. Tools like random forest have an established track record of off-the-shelf success and even offer various strategies for analyzing the underlying relationships between features and the response. Motivated by recent insights into random forest behavior, here we introduce the idea of augmented bagging (AugBagg), a procedure that operates in an identical fashion to the classical bagging and random forest counterparts but which operates on a larger space containing additional, randomly generated features. Somewhat surprisingly, we demonstrate that the simple act of adding additional random features into the model can have a dramatic beneficial effect on performance, sometimes outperforming even an optimally tuned traditional random forest. This finding that the inclusion of an additional set of features generated independently of the response can considerably improve predictive performance has crucial implications for the manner in which we consider and measure variable importance. Numerous demonstrations on both real and synthetic data are provided.